#rpa case studies in banking

[learning-robotics]

What is the future of AI in IA

Explore how AI is revolutionizing the field of internal audit, enhancing efficiency, accuracy, and risk management. Discover the benefits, challenges, and future trends of AI in internal auditing.

The Future of AI in Internal Audit

Artificial Intelligence (AI) and Internal Audit (IA) are two domains that, at first glance, might seem worlds apart. AI refers to the simulation of human intelligence in machines designed to think and learn like humans. Internal auditing, on the other hand, involves the examination and evaluation of an organization's internal controls and processes. But as AI technology advances, its integration into internal audit practices is becoming increasingly significant. Let's dive into the transformative impact AI is having on internal audit and what the future holds.

The Evolution of Internal Audit

Traditionally, internal auditing has relied heavily on manual processes. Auditors would comb through documents, interview employees, and scrutinize financial transactions. This method, while thorough, is time-consuming and prone to human error. Moreover, as businesses grow and become more complex, the volume of data to be audited increases exponentially, posing significant challenges to traditional auditing methods.

Introduction of AI in Internal Audit

The integration of AI in internal audit started with the need to address these challenges. Early adopters of AI technology in auditing began to see immediate benefits, such as increased speed and accuracy in data analysis. AI systems could quickly sift through vast amounts of data, identifying patterns and anomalies that would take human auditors much longer to detect.

Key AI Technologies Used in Internal Audit

Several AI technologies are particularly relevant to internal audit:

Machine Learning (ML): Enables systems to learn from data and improve their performance over time without explicit programming.

Natural Language Processing (NLP): Helps in analyzing and understanding human language, making it easier to process documents and communications.

Robotic Process Automation (RPA): Automates repetitive tasks, allowing auditors to focus on more complex issues.

Predictive Analytics: Uses historical data to predict future outcomes, aiding in risk assessment and decision-making.

Benefits of AI in Internal Audit

AI brings numerous benefits to internal audit:

Enhanced Efficiency and Accuracy: AI systems can process large datasets quickly and accurately, reducing the time and effort required for audits.

Real-time Data Analysis and Reporting: AI allows for continuous monitoring and real-time reporting, providing timely insights and reducing the risk of fraud or errors going unnoticed.

Improved Risk Management: AI can identify potential risks and issues before they become significant problems, allowing organizations to take proactive measures.

Cost Savings and Resource Optimization: By automating routine tasks, AI frees up human auditors to focus on higher-value activities, optimizing the use of resources.

AI-Driven Audit Processes

AI is transforming various audit processes:

Automated Transaction Monitoring: AI systems can continuously monitor transactions for signs of fraud or anomalies, flagging suspicious activities for further investigation.

Continuous Auditing and Continuous Control Monitoring: AI enables ongoing evaluation of controls and processes, ensuring they remain effective over time.

Fraud Detection and Prevention: AI can detect patterns indicative of fraudulent activities, helping organizations prevent losses and maintain integrity.

Case Studies of AI in Internal Audit

Many organizations have successfully integrated AI into their internal audit processes:

Financial Institutions: Banks use AI to monitor transactions for signs of money laundering and other fraudulent activities.

Retail Companies: Retailers leverage AI to analyze sales data, ensuring compliance with internal policies and identifying discrepancies.

Manufacturing Firms: Manufacturers employ AI to monitor supply chain activities, improving efficiency and reducing risks.

These case studies highlight the versatility and effectiveness of AI in various industries.

Challenges and Limitations of AI in Internal Audit

Despite its many benefits, AI also presents challenges:

Data Privacy and Security Concerns: Ensuring the security of sensitive data processed by AI systems is crucial.

Integration with Existing Systems: Integrating AI with legacy systems can be complex and costly.

Skills Gap and Training Requirements: Auditors need training to effectively use AI tools and interpret their outputs.

Future Trends in AI for Internal Audit

The future of AI in internal audit looks promising:

Increasing Adoption and Innovation: More organizations will adopt AI, driving further innovation in audit practices.

Enhanced Collaboration Between AI and Human Auditors: AI will complement human skills, with auditors focusing on tasks requiring judgment and expertise.

Evolution of AI Regulations and Ethical Standards: Regulatory frameworks will evolve to address the ethical implications of AI in auditing.

Preparing for the Future

To prepare for the AI-driven future, internal audit departments should:

Embrace Continuous Learning: Stay updated on AI advancements and invest in training.

Adopt a Strategic Approach: Develop a clear strategy for integrating AI into audit processes.

Foster a Collaborative Culture: Encourage collaboration between AI experts and auditors.

AI and Regulatory Compliance

AI can significantly enhance compliance efforts:

Adhering to Regulatory Requirements: AI helps ensure that organizations comply with relevant laws and regulations by continuously monitoring compliance.

Impact on Compliance Audits: AI simplifies the audit process, making it easier to verify compliance.

Ethical Considerations

While AI offers many benefits, ethical considerations are essential:

Balancing Automation with Human Oversight: Ensure that AI systems are used responsibly, with human auditors providing oversight.

Ethical Use of AI in Auditing Practices: Maintain transparency and accountability in AI-driven audits.

The Role of Internal Auditors in an AI-Driven World

The role of internal auditors will evolve:

Shifting Roles and Responsibilities: Auditors will focus more on strategic tasks and less on routine data analysis.

Importance of Human Judgment: Despite AI's capabilities, human judgment remains crucial in interpreting findings and making decisions.

Conclusion

AI is set to revolutionize the field of internal audit, offering significant improvements in efficiency, accuracy, and risk management. As organizations continue to adopt and innovate with AI, internal auditors must adapt to these changes, embracing new technologies while maintaining ethical standards and human oversight. The future of AI in internal audit is bright, promising enhanced audit processes and better organizational outcomes.

FAQs

What is the role of AI in internal audit? AI enhances internal audit by automating data analysis, improving accuracy, and providing real-time insights.

How does AI improve the efficiency of internal audits? AI processes large datasets quickly, reduces manual efforts, and continuously monitors transactions for anomalies.

What are the challenges of implementing AI in internal audit? Challenges include data privacy concerns, integration with existing systems, and the need for specialized training.

Can AI completely replace human auditors? No, AI complements human auditors by handling routine tasks, allowing them to focus on strategic and judgment-based activities.

How can internal audit departments prepare for AI integration? By investing in continuous learning, developing a strategic approach to AI integration, and fostering collaboration between auditors and AI experts.

[ellicium]

What's the future of robotic process automation in the finance industry?

In today's dynamic business landscape, finance and accounting professionals are undergoing a significant shift. They're no longer confined to traditional roles but are expected to be strategic partners driving business insights. This transformation is happening amidst rapid technological advancements, with robotic process automation (RPA) emerging as a pivotal tool. 

While finance might not be the obvious choice for RPA implementation, studies show that many finance executives are either implementing or planning to implement RPA, highlighting its potential to revolutionize the finance industry.

The Role of Finance in Modern Business:

Finance and accounting functions have evolved beyond their traditional transactional roles. Today, they play a crucial role in providing strategic insights and guiding business decisions. With vast financial data, finance professionals are uniquely positioned to analyze trends, forecast future scenarios, and drive growth initiatives. However, they must embrace technological innovations that streamline processes and enhance efficiency to fulfill these expectations effectively.

How is RPA used in Finance and Accounting?

As finance and accounting evolve into strategic business partners, adopting innovative technologies like robotic process automation (RPA) becomes imperative. RPA offers a plethora of use cases in finance and accounting, revolutionizing traditional processes and enhancing efficiency. Let's delve into some common applications of RPA in these domains to understand its transformative potential:

Accounts Receivable:

RPA streamlines the creation, sending, and tracking of invoice payments, reducing errors and expediting payment processing. 

Accounts Payable:

Accounts payable functions entail verifying invoices against purchasing orders, a time-consuming and data-intensive process. It automates invoice distribution, scheduling reminders, and cross-checking invoices with purchasing orders, facilitating accurate and efficient payment processing. This eliminates manual errors and enhances compliance with payment schedules.

Client Onboarding:

Compliance with Know Your Customer regulations necessitates thorough due diligence before onboarding new customers. RPA bots expedite data collection from various systems, generate detailed reports for compliance review, and seamlessly transfer approved customer information into CRM systems. This accelerates the onboarding process while ensuring regulatory compliance.

Financial Statements and Financial Close:

RPA enables finance departments to generate up-to-date financial statements and expedite the financial close process. By automating data collection and transformation, RPA significantly reduces the time required for financial reporting and analysis. This empowers business leaders with timely insights for informed decision-making.

Planning and Forecasting:

Accurate financial planning and forecasting rely on comprehensive data analysis. RPA aggregates historical data from disparate sources, facilitating seamless data processing and analysis. By leveraging automation solutions, finance professionals can quickly generate precise financial forecasts and analyze variance.

Travel and Expense Processing:

Managing travel expenses can be time-consuming for accounting departments. RPA automates expense report processing by validating submissions against internal policies, expediting reimbursement approvals, and flagging non-compliant claims. This ensures compliance with expense policies while enhancing process efficiency.

Account Reconciliations:

RPA simplifies account reconciliations by automating data comparison and anomaly detection. Whether it's intercompany reconciliations or bank statement reconciliations, RPA accelerates the reconciliation process, reduces errors, and improves audit readiness. This guarantees the precision of financial reports while bolstering adherence to regulatory standards.

Data Management:

Effective data management is pivotal for informed decision-making and optimizing customer service experiences. It automates data movement and transformation across systems, facilitating process execution, analysis, and reporting. 

Conclusion:

In conclusion, Robotic Process Automation (RPA) stands poised as the cornerstone of future-proof finance and accounting operations. Its transformative prowess lies in its capacity to streamline workflows and liberate resources and data management systems. 

By embracing RPA, enterprises can unlock efficiencies, empowering finance professionals to channel their expertise toward strategic pursuits. If your organization has yet to embark on the RPA journey, seize the moment to chart a course toward enhanced productivity and competitive edge. 

With RPA as your steadfast ally, you're primed to navigate the evolving finance landscape with dexterity and innovation. Embrace the transformative potential of RPA today and propel your operations towards unprecedented heights of excellence and agility.

[sun-technologies]

Maximizing Efficiency with Contract AI and O2C Automation

Contract AI (Contract Artificial Intelligence):

Contract AI refers to using machine learning and artificial intelligence (AI) technologies to streamline and optimize the management of contracts throughout their lifecycle. It involves the application of AI algorithms to analyze, extract, and interpret data from contracts, automate contract-related processes, and improve contract risk management and compliance. Contract AI aims to enhance efficiency, reduce manual labor, mitigate risks, and facilitate better decision-making in managing legal agreements and contracts within organizations.

O2C Automation (Order-to-Cash Automation):

O2C Automation, also known as Order-to-Cash Automation, implements automated processes and technologies to optimize and streamline the entire order-to-cash cycle in a business. This cycle encompasses all the steps in fulfilling customer orders, from order initiation to payment receipt. O2C Automation typically involves robotic process automation (RPA), workflow automation, and data analytics to improve order processing efficiency, reduce errors, enhance cash flow management, and provide a better customer experience. It plays an important role in financial and customer relationship management within organizations, particularly in the BFSI (Banking, Financial Services, and Insurance) sector. Explain their significance in the BFSI (Banking, Financial Services, and Insurance) sector.

Discuss the need for modernization in BFSI

Modernization in the Banking, Financial Services, and Insurance (BFSI) sector is imperative due to the convergence of technological innovation, shifting customer expectations, stringent regulatory demands, and heightened competition. BFSI institutions must embrace digital transformation to stay relevant and competitive, providing seamless and personalized services while adhering to evolving regulatory frameworks. Modernization enhances operational efficiency, enables robust risk management, fosters innovation, and facilitates cost savings, ultimately ensuring that organizations can adapt to a rapidly changing financial landscape and deliver value to their customers and shareholders.

Benefits of Contract AI in BFSI

Improved contract management

Enhanced compliance and risk management

Faster contract review and approval

Cost savings through automation

Case studies of BFSI companies benefiting from Contract AI

Benefits of O2C Automation in BFSI

Streamlined order processing

Improved cash flow management

Enhanced customer experience

Reduced errors and fraud prevention

Real-world examples of O2C Automation success stories in BFSI

Challenges in Implementing Contract AI and O2C Automation

Certainly, here are strategies to overcome the challenges associated with implementing Contract AI and Order-to-Cash (O2C) Automation in the Financial Services, Banking, and Insurance (BFSI) sector:

Data Privacy and Security Concerns:

Data Encryption: Implement robust data encryption techniques to protect sensitive contract and financial data in transit and at rest. Use industry-standard encryption protocols to secure data.

Access Control: Implement strict access controls and role-based permissions to make sure that only authorized personnel can access sensitive information. Regularly audit and monitor access to identify any unauthorized activity.

Data Privacy Compliance: Ensure your systems and processes comply with data privacy regulations such as GDPR. Conduct regular privacy impact assessments to mitigate and identify potential risks.

Integration with Legacy Systems:

APIs and Middleware: Invest in middleware solutions or develop APIs to bridge the gap between modern Contract AI and O2C Automation systems and legacy systems. This allows for smoother data exchange and process integration.

Gradual Migration: Consider a phased approach to integration, where you gradually migrate specific processes or functions to the new system, reducing the immediate burden on legacy systems.

Customization: Tailor integration solutions to the specific needs of your organization. Custom development may be necessary to ensure seamless connectivity.

Staff Training and Change Management:

Comprehensive Training: Provide comprehensive training programs for employees who will be using the new Contract AI and O2C Automation systems. Training should cover both the technical aspects and the benefits of the new systems.

Change Champions: Identify and train "change champions" within your organization—individuals who can champion the adoption of new technologies and processes and help colleagues adapt.

Continuous Learning: Foster a culture of adaptation and continuous learning. Encourage employees to keep up with technology trends and actively seek feedback to improve processes.

Regulatory Compliance Challenges:

Regulatory Expertise: Employ or consult with regulatory experts who have a deep understanding of the BFSI sector. They can assist you in navigating complex compliance requirements and keeping your systems up to date.

Automated Compliance Monitoring: Utilize automation and AI for real-time monitoring of regulatory compliance. Implement compliance checks and alerts within your Contract AI and O2C Automation systems.

Regular Audits: Conduct regular audits of your systems and processes to ensure compliance. Document compliance efforts to demonstrate due diligence in case of regulatory inquiries.

Strategies to Overcome These Challenges:

Cross-Functional Teams: Form cross-functional teams involving IT, legal, compliance, and business units to collaboratively address challenges and ensure a holistic approach to implementation.

Pilot Programs: Begin with small-scale pilot programs to test the effectiveness of Contract AI and O2C Automation solutions while identifying and addressing issues on a smaller scale before full-scale deployment.

Third-Party Expertise: Consider partnering with experienced technology vendors or consultants who specialize in BFSI automation. They can provide valuable guidance and insights throughout the implementation process.

Continuous Improvement: Implement continuous improvement practices to refine processes, enhance data security, and adapt to changing regulations and technology advancements over time.

Communication: Maintain open and transparent communication with stakeholders at all stages of implementation. Address concerns and provide regular updates to build confidence in the new systems.

Documentation: Keep detailed records of your implementation process, including decisions, changes, and compliance efforts. This documentation can be invaluable for audits and ongoing improvement.

Technologies Behind Contract AI and O2C Automation

Natural Language Processing (NLP)

Machine Learning and Predictive Analytics

Robotic Process Automation (RPA)

Blockchain for contract security

Cloud computing for scalability

Steps to Implement Contract AI and O2C Automation

Assessing your organization's readiness

Selecting the right technology and vendors

Developing a phased implementation plan

Training and upskilling your workforce

Measuring and optimizing the implementation's success

Future Trends and Innovations

AI-driven chatbots for customer inquiries

Smart contracts and decentralized finance (DeFi)

Predictive analytics for financial forecasting

AI in risk assessment and fraud detection

Conclusion and Future Outlook

In conclusion, Contract AI and Order-to-Cash (O2C) Automation stand as transformative forces in the Banking, Financial Services, and Insurance (BFSI) sector, poised to redefine how contracts are managed and financial processes are streamlined. Despite the challenges, these technologies offer the promise of heightened efficiency, accuracy, compliance, and customer satisfaction. As BFSI organizations navigate the complexities of data privacy, legacy system integration, staff adaptation, and regulatory adherence, they must recognize that embracing these innovations is not an option but a necessity for remaining competitive and resilient in an ever-evolving financial landscape. The successful implementation of Contract AI and O2C Automation holds the potential to revolutionize the BFSI sector, shaping a future where financial operations are faster, more secure, and aligned with the demands of a digital-first world.

[bpmindustry]

Rhythm of Finances: Exploring BPM Banking Strategies

In today's fast-paced financial landscape, the banking industry is continuously evolving to meet the dynamic needs of customers while adapting to technological advancements. One key aspect that defines the success of modern banking institutions is their ability to synchronize their operations with the rhythm of finances. Business Process Management (BPM) has emerged as a critical strategy that enables banks to harmonize their processes, enhance customer experience, and drive efficiency. In this blog, we delve into the depths of BPM banking strategies, exploring how they reshape the industry.

Understanding Business Process Management (BPM) in Banking: Business Process Management refers to the systematic approach of identifying, designing, executing, and continuously improving business processes within an organization. In the banking sector, BPM involves streamlining various operations, from customer onboarding and loan processing to risk assessment and compliance monitoring. By orchestrating these processes, banks can enhance their agility, reduce costs, and create a seamless customer journey.

The Key Pillars of BPM Banking Strategies:

Process Optimization: Banks are intricate networks of interconnected processes. BPM strategies help identify bottlenecks, redundancies, and inefficiencies, allowing institutions to re-engineer and optimize processes for improved performance.

Customer-Centricity: The heart of BPM banking strategies lies in creating a customer-centric approach. By mapping customer journeys and analyzing touchpoints, banks can align their processes to meet customer needs effectively.

Risk Management and Compliance: Ensuring regulatory compliance is a critical aspect of banking operations. BPM facilitates the integration of compliance checks into processes, reducing the risk of errors and penalties.

Data-Driven Decision Making: Data is the lifeblood of modern banking. BPM strategies leverage data analytics to provide insights into process performance, enabling banks to make informed decisions and predictions.

Benefits of BPM in Banking:

Enhanced Customer Experience: BPM strategies enable banks to deliver consistent and personalized services, creating a positive customer experience across various touchpoints.

Operational Efficiency: Streamlined processes reduce operational friction, leading to quicker service delivery and lower costs.

Adaptability: The banking industry is subject to constant changes. BPM equips banks with the agility to swiftly adapt to market shifts, regulatory updates, and technological advancements.

Innovation: BPM encourages a culture of continuous improvement, fostering innovation within banking processes.

Case Study: Implementing BPM in Mortgage Processing Let's consider the example of a bank's mortgage processing department. Through BPM implementation, the bank managed to automate document collection, verification, and approval processes. This resulted in a significant reduction in processing time, enabling quicker loan disbursals. Moreover, the bank integrated compliance checks directly into the process, minimizing the risk of errors and ensuring adherence to regulations.

Challenges and Future Trends: While BPM offers substantial benefits, its implementation comes with challenges. Legacy systems, change resistance among employees, and the need for ongoing process refinement are some hurdles banks might face. Looking ahead, the future of BPM in banking is likely to involve more advanced technologies, such as robotic process automation (RPA), artificial intelligence (AI), and blockchain. These technologies can further enhance process efficiency and accuracy.

Conclusion: In the ever-evolving landscape of banking, embracing the rhythm of finances through BPM strategies is essential for success. By optimizing processes, focusing on customer needs, and staying ahead of regulatory changes, banks can achieve operational excellence and provide outstanding services. As technology continues to advance, the marriage between BPM and cutting-edge innovations will shape the future of banking, driving growth and transformation.

Remember, the journey towards effective BPM implementation requires commitment, adaptability, and a customer-centric mindset – the key elements that ensure the rhythm of finances remains in harmony with the ever-changing world of banking.

SITES WE SUPPORT

BPM Industry – Weebly

[dvtsa46]

How Robotic Process Automation Rpa Will Transform Finance

If you are contemplating incorporating RPA bots into a few of the monetary processes in your group, step one is to analyze and standardize manual processes. With this information readily available benefits of rpa, it is going to be simpler to configure the bots to know exactly what to do . You must mandate work standardization for all workers simultaneously you’re implementing the robots.

It may include evaluation of appropriate RPA instruments for your organisation as well. RPA is expertise which performs processes shortly and precisely, saving time in order that staff rpa companies can return again to the core of their roles and carry out actual value added work. While RPA has impressive results for many companies, there are tons of examples where RPA implementations have failed.

Robotic Process Automation automates repetitive duties; automated invoice processing helps save time and human effort. It is infusing greater efficiency with elevated accuracy of captured knowledge. By decreasing handbook efforts, organizations are lowering costs incurred and in addition considerably increasing the income. RPA in bill automation has enhanced the overall productiveness of the group, cash flow, and stakeholder relations. RPA is for automating enterprise processes which may be performed on computer techniques.

I consider that soon, RPA in finance processes will convert from being “nice to have” into being “need to have” for each type of organizations. It’s also essential to be aware, that even though you might believe that a sure process isn’t suited for RPA, then attempt twisting your brain rpa solutions slightly and take into consideration in what components of that process RPA may be utilized. Trust me, an opportunity may be discovered virtually anywhere, and it’s truly a good idea to start with a step by step model.

With zero error charges and no adjustments to underlying enterprise systems, purposes or processes, deploying your digital workforce of bots takes a matter of weeks, and exhibits a return on funding in a matter of days. You have full control over your bots – start, cease and scale your RPA robots on demand. RPA for finance and bankinghas reduced human error which may be very dangerous for monetary companies. An automated process ensures that all transactions are executed at maximum pace with accuracy. This reduces errors within the finance business, which can lead to losses if not detected early. However, physical robots - those that we see and pay attention to - aren't the one ones on the market helping.

Salesforce research underlines that the overwhelming majority of IT managers really feel a low-code solution retains their business companion relationships extra optimistic. The financial sector is one such domain that’s riddled by vital and complicated processes, the huge bulk of which are monotonous. Therefore, robotic process automation is the right suited solution on this case. Before shifting ahead, let’s understand what robotic process automation means. Well, to put it in simple words, it is a robot that performs the same task as a human however extra effectively and rapidly. Although robotic process automation just isn't changing individuals with robots, it's serving to them focus on core methods and complex problem-solving by automating repeated and redundant every day tasks.

Robotic process automation companies are often confused with synthetic intelligence , however both are completely different. AI is combination of automation, machine studying , pure language processing , reasoning, speculation era and evaluation. Banks are consistently in search of methods to enhance buyer experience, keep a aggressive edge and facilitate seamless and speedier processes. But what can robotic process automation in finance be the most effective digital remedy to handle all of these key apprehensions? Robotic Process Automation in accounting, finance and auditing offers you the ability to improve productiveness, drive down costs and streamline compliance. It frees up time for you and your staff to behave proactively and focus on the strategic work that brings joy and provides value to your small business.

[tutorsindia152]

Economic and finance research topics, Compiled for UK university student

Choosing a unique research topic is one among the essential decisions made by researchers. Some research scholars may spend a year or even more looking for various thesis or dissertation topics before deciding on one. Regardless of academic discipline, there are a lot of effective ways for finding research topics.

Discovering a research topic requires reviewing numerous types of literature, but selecting a research topic requires identifying the most important factors and evaluating their value against the vast number of options available.

Research topic using previously published literature

A literature review is a quick overview of the current quantity of knowledge on the subject/topic of interest.  [5] It will provide a theoretical foundation for some techniques and act as a reference for the process of innovation. [6] It acts as a guide for the researchers in review and selection of important search terms.

Another advantage of a good literature review is that it reveals  a variety of methodologies used in prior similar researches. Also, it supports the candidate in developing a strong dissertation methodology. Thankfully, since the advent of  internet, a variety of online resources have become available for a seamless review with the essential guidance.

Points to note when choosing a topic:

Pick an     interesting topic

Be aware     of ongoing research

Define     your constructs clearly

Evaluate     whether your topic is publishable

Evaluate     your topic’s importance

Establish     a timeline for completion

The dissertation topics are selected based on the research gap and future recommendations proposed by previous researchers. Tutors India topic selection is driven by your research question that is interested in exploring.

 Some of the trending research areas from where you can choose your master’s Economics research topic

Topics on Covid-19 and its Effect on Financial Services

A Review     Analysis and Policy Recommendations on Microeconomic Impacts of COVID-19     Pandemic

Rahman Tisha, T. (2021). Microeconomic Impacts of COVID-19 Pandemic: A Review Analysis and Policy Recommendations (preprint).

2. Corporate Survival and Public Policy: The Role of Accounting Information and Regulation in the Wake of a Systemic Crisis Covid-19

Buchetti, B., Parbonetti, A., & Pugliese, A. (2021). Covid-19, Corporate Survival and Public Policy: The Role of Accounting Information and Regulation in the Wake of a Systemic Crisis. Journal of Accounting and Public Policy, 106919.

Crypto currency

A     Comparative Analysis on Blockchain Cryptocurrencies’ Price Movements     Through Deep Learning

Uras, N., & Ortu, M. (2021, March). Investigation of Blockchain Cryptocurrencies’ Price Movements Through Deep Learning: A Comparative Analysis. In 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER) (pp. 715-722). IEEE.

2. The Rloe of Machine learning in Short-term bitcoin market prediction and cryptocurrency returns

Jaquart, P., Dann, D., & Weinhardt, C. (2021). Short-term bitcoin market prediction via machine learning. The Journal of Finance and Data Science, 7, 45-66.

Audit in Financial Services

The role     of automation and Robotic process automation (RPA) in the auditing     industry

Wewerka, J., & Reichert, M. (2021). Robotic process automation-a systematic mapping study and classification framework. Enterprise Information Systems, 1-38.

2. The significance of auditors in financial reporting and  Role of Forensic Audit in Controlling Financial Statement Fraud

Mishra, K., Azam, M. K., & Junare, S. O. (2021). Role of Forensic Audit in Controlling Financial Statement Fraud: A case study of Satyam Computers. Psychology and Education Journal, 58(2), 4016-4025.

Risk Management: Capital, Liquidity, ALM, Models

Overview     on liquidity risk management and financial crisis in the UK banking     industry

Al Janabi, M. A. (2021). Liquidity risk management in the avoidance of another financial crisis. Available at SSRN 3840350.

2. The structure, scope, and assessment of risk management in European international banking functions

Adam, M., Soliman, A. M., & Mahtab, N. (2021). Measuring Enterprise Risk Management implementation: A multifaceted approach for the banking sector. The Quarterly Review of Economics and Finance.

If you find difficulty with research topic selection, you can contact tutors India, our experts help you in coming up with innovative, trendy research topics in your area of research.

Internet Banking and Digital Journey for Banks

The     security challenges associated with electronic banking transactions

Rehman, T. U. (2021). Theoretical Context of E-Banking for Digital Enterprise Transformation. In Disruptive Technology and Digital Transformation for Business and Government (pp. 91-109). IGI Global.

2. An evolving relationship between FinTech and banking industry

Carbó-Valverde, S., Cuadros-Solas, P. J., & Rodríguez-Fernández, F. (2021). FinTech and banking: An evolving relationship. In Disruptive Technology in Banking and Finance (pp. 161-194). Palgrave Macmillan, Cham.

Ethics in Accounting

Ethics or     profit followed among the accountants in emerging firms

Nguyen, L. A., Dellaportas, S., Vesty, G. M., Jandug, L., & Tsahuridu, E. (2021). The influence of organisational culture on corporate accountants’ ethical judgement and ethical intention in Vietnam. Accounting, Auditing & Accountability Journal.

  2. A case study about connection between Audit risk and Rhetoric of rationality

Mökander, J., Axente, M., Casolari, F., & Floridi, L. (2021). Conformity assessments and post-market monitoring: a guide to the role of auditing in the proposed European AI Regulation. Minds and Machines, 1-28.

Microfinance

Microfinance     impact on the microenterprises sector

Nor, M. M. (2021). THE IMPACT OF MICROFINANCE TOWARDS MICROENTERPRISES (MEs) PRODUCTIVITY IN MALAYSIA. International Journal of Humanities Technology and Civilization, 68-82.

2. Evidence from Pre and Post-Financial Crisis on Impact of Macroeconomic Conditions over Microfinance Institutions  Financial Efficiency

Roslan, S., Zainal, N., & Mahyideen, J. M. (2021). Impact of Macroeconomic Conditions on Financial Efficiency of Microfinance Institutions: Evidence from Pre and Post-Financial Crisis. The Journal of Technology Management and Technopreneurship (JTMT), 9(1), 32-47.

Retail and Commercial Banking [1]

How were     the policies of local commercial banks in Europe changed throughout the     period?

How is     consumer demand influencing SME choices and strategies in the UK?

A case     study of developing countries inventory management for mobile banking

In the     banking industry, what is the relationship between pricing, equity, and     performance?

Financing in Emerging Market [2]

A     comparative study on FDI strategies between Asia and European countries?

A case     study  on interlinks economic growth and population in heavily     populated countries like China

The     development and the existing state of investment banking over the emerging     market sectors

What are     the difficulties faced by financial institutions in a developing economy?

The     perception and attitude of investors from developed countries toward     emerging market investment opportunities

Tutors India Research Topic Selection Services provides you professional assistance and guidance while choosing a research topic. The topics we provide would offer you a clear and precise understanding of the proposed research area and ensure that you move in the right direction.

Conclusion

The finance dissertation topic that you choose for your research work has a direct impact over your career. The topic you choose for your finance dissertation is quite important in determining your final grade. The standard of research and writing, on the other hand, is as important.

[rpatech]

4 ways in which financial institutions are leveraging RPA for fraud detection

The Banking and Financial Services sector is heavily reliant on standard processes and systems. The BFSI sector is also one that is highly susceptible to fraud and fraudulent behavior. Especially in the wake of COVID-19 and new digital initiatives, there has been a rise in incidents of fraud and the threat is only looking large. As per a Deloitte India survey, the key reasons leading to the rising cases of fraud include the large-scale remote work models, increasing customers leveraging non-branch banking channels, and the ineffective use of forensic analytics tools to find and mitigate potential frauds or threats. The solution lies with leveraging RPA in Fraud Detection that can reduce the overheads associated with traditional fraud prevention processes and get faster, more accurate results.

Failure of traditional models of fraud detection

A majority of banks and financial institutions use rules-based systems with manual evaluation for identifying fraud. These methods worked well in the past but with fraudsters and cyber criminals becoming more sophisticated, the older methods for identifying and mitigating risks are simply not fast enough.

Over the years, fraud patterns have transformed and evolved faster than the rules-based processes. This has led to multiple issues. For instance, false positives or undetected incidents as a result of the high volume of banking data. This is where robotics process automation comes to the rescue.

Implementation of RPA in fraud detection

RPA solutions are ideal for use cases that need to analyse vast reams of data quickly and then apply rules-based actions to it. RPA is then ideal for the automated, repetitive and rules-based financial macros of fraud detection.

Not only is this solution cheaper and faster, it is more efficient as it gets rid of the manual errors and enables the workforce to focus on less tedious and more creative work.

Here’s how implementing RPA can help in fraud identification and mitigation:

1. Reassessing current processes

Banks and financial organisations can program RPA bots to review current and historical financial transactions to find anomalies and atypical patterns that can indicate illegal or fraudulent activities.

This also helps at the broader business level because implementing RPA will require the financial institution to study, document, assess the current processes, leading to deeper insights and identifying high-risk areas.

2. Elimination of human errors

By strategically incorporating RPA into the business processes, banks and financial institutions can significantly reduce the human interaction and chances of manual errors. Employers can instead focus on other important tasks, eliminating the chance of clerical errors.

3. Improved trade monitoring

With a rise in financial crimes and money laundering, many companies and government agencies seek the help of intelligent automation to fight financial terrorism. RPA bots, when integrated with other automation technologies, can process each and every transaction for potential fraud and flag high-value transactions as appropriate. RPA’s capability lies in sifting unstructured data and the bots can identify risks much faster than a manual process.

4. Automated threat detection

Monitoring thousands of web pages or reams of application data would be a huge task for any fraud detection team. However, this task would be much simpler, faster, and easier for a bot.

Two of the common use cases where RPA solutions can be leveraged to implement automated threat detection include:

● Copyright infringement

RPA bots prevent copyright infringement by quickly scanning through suspected websites for your confidential or copyrighted information such as patents, trade secrets etc.

● Pricing information

In order to boost sales and steal your customers, many websites or companies may sell your products at lower price points that are impossible to match. One thing that RPA bots do well and quickly is to collect and aggregate pricing information to see whether your offerings are being sold online illegally or set at lower price points.

Why should RPA be used for fraud mitigation?

Fraud impacts various organizations in multiple ways. When an RPA solution is integrated into a well-structured process, the human element can be minimized, leading to faster processes, fewer errors, and improved customer experiences. A survey of 164 Financial Executive International members shows that RPA is underutilized to prevent and detect fraud. The survey highlights some benefits that companies hoped to achieve through RPA. These included reducing employee time on low-value-added tasks, mitigating human errors, and reducing costs.

Benefits of using RPA for fraud detection

Implementing fraud detection bot, banks and financial institutions can see a noticeable difference in the ROI of this automated labour. For instance, time spent in processing requests can be reduced to 50% and exception rate can be slashed to zero. Some of the other benefits include enhanced service-level agreements, reducing hours of manual labour to minutes of automated work, elimination of human involvement in bulk processing etc.

Final thoughts

RPATech’s customized financial bot solutions don’t just help your organisation to save on costs or work faster, it helps you to work smarter. By adopting RPA, you join the league of several other industries and create a competitive advantage. If you want to understand how RPA adoption creates a win-win situation for both your employees and customers, get in touch with us for a consultation.

[simplesolvecom]

Brain vs Bot:  Does RPA in Insurance Need Intelligent Automation?

It is a well-recognized fact that the insurance workforce spends an average of 15% of their time on repetitive tasks. While these are pretty important tasks, such as manual underwriting or customer database updates, the point is that they are not exactly bringing in new customers or even helping personalize the customer experience, neither are they saving costs. Yet, they must be done, so just get on with it, right? Wrong, it might need to be done but why have humans do them if there is a better alternative like Robotic Process Automation.  

Financial sector case studies (McKinsey) have shown a 200% growth in ROI in the first year of RPA deployment. This then is the reason why RPA has already got a wide adoption in the insurance industry to automate repeatable tasks in back-office operations. However, an RPA bot cannot ‘think’ and that is why it is more of a short-term solution. Insurance carriers looking to move to a digital transformation path need to use RPA coupled with intelligent process automation.

Why RPA can only be a starting point

Although RPA is used in almost all industries, the biggest adopters are in the banking sector, insurance carriers, and utility companies (Gartner). The reason is clear, these industries have a large number of well-defined processes that make them highly suitable for robotic process automation. To understand how insurance carriers are using RPA, we need to first understand how RPA works because that is where its limitations also become visible. 

RPA is rule-based software that has no intelligence built into it. RPA technology predominantly uses UI (user interface) to create specific low-code scripts to automate routine tasks - tasks, that at most, have limited variations so that a finite set of rules will define what must be done. Visualize a bottling industry where assembly line machines work faster than humans and without a break - doing the same work day after day. That is exactly the same when it comes to RPA and business processes in insurance workflows. Take for instance claims processing, it requires companies to collect data from a large number of sources. It is exhausting but it is based on well-defined structural inputs and that is why programming languages can be written to automate data collection.

The challenge though is that RPA technology is too rigid, after all, it is based on a finite set of rules. Introducing RPA to replicate human tasks requires companies to keep adding new bots to cover new areas in which the previous technology was not programmed to handle. Further, processes change over time, RPA bots cannot pick up on these changes and have to be reconfigured for the particular change. That’s why while RPA is incredibly useful it is only part of the solution and not the full solution. 

RPA needs to be augmented with cognitive intelligence to train the bots to learn from experience and evolve. Tech-driven startups like  Lemonade and Metromile are making traditional insurance carriers realize that digital innovation requires machine learning and artificial intelligence to be woven into more and more workflows.

Hand-picked for you: Can Automated Underwriting Impact Quality of Your Customer Base?

Adding brains to the bots

Traditional RPA automation is applied to repetitive tasks but when AI is combined with RPA (RPA is considered a subset of artificial intelligence) then the process can even identify exceptions and deal with them without any external input. It can analyze large volumes of data, both external and internal, and can pull out insights that can be translated into specific actions. 

I am sure you are getting the picture but let’s look at how it works in an automated claims processing workflow. Typically, each claim would take several days as data must be gathered from different sources, for instance, driver’s license, police reports, and vehicle damage pictures in case of an auto claim. Intelligent process automation begins from the point of claims intake, assessment, and final settlement of the claim. When the system receives the first notice of loss, the RPA bot will extract all the information from the request sent in and enter details into the claims system. This process has already been working for years but RPA working along with intelligent optical character recognition (OCR) software will automatically read information from unstructured documents like emails or even phone transcripts or paper documents. Now enters a cognitive bot that will check the form to see if there are any missing details. If there is none, and if it is below a previously set threshold amount, it is automatically approved and sent to the payments department. However, if there is missing information and the AI bot is meeting it for the first time, it sends it to a human agent and ‘watches and learns’ how the agent resolves it. The next time it comes across the same exception, it knows what to do. This 'learning' is what sets apart the new process from the older versions.

Almost 20-30% of automated processes require exception handling

This is where RPA works best when it has intelligent automation on its side. Robotic Process automation workflows are always linear. However, business processes rarely have a linear progression and AI coupled with RPA provides the ability to take different paths, each having a successful outcome. AI/ML algorithms perfect the exception handling mechanism over time to be able to work with the least amount of human supervision.

Automated claims process, reduces manual work by 80%, cuts down the time needed for processing the claim by 50% and reduces the cost of a claims journey by 30%.

Also of interest: Big Fish Alternatives in P&C Insurance Core Systems

The evolution of RPA

Initially, RPA bots were unattended as the rules were defined and they were programmed to complete a specific task. Today, this has changed with more attended bots at work. This is because intelligent process automation is enabling them to work as personal assistants to human agents. These cognitive bots are being used to collect relevant data in near-instant time and provide it to humans. For instance, in a customer call, all information related to the customer’s request can be presented in a matter of seconds. The same goes for underwriting where more personalized plans can be provided when all data is quickly collated.

By 2030, manual underwriting will no longer exist for most personal products across life and P&C insurance - McKinsey

AI-RPA pairing is also being integrated into chatbots. Simplesolve’s own interactive bot, Tara, has been built to support intelligent underwriting. She also assists agents in upselling additional coverages and limits based on data in the insurance application.

The biggest benefit for traditional insurers is that RPA creates an automation layer above the existing legacy systems. AI is then an extra decision layer added over this to automate the BPM process. While modernizing systems for a full digital transformation is important in the long run, intelligent automation in insurance can still be enabled with existing systems to optimize workforce processes for better efficiency.  

Topics: Intelligent Automation

Source URL: -  https://www.simplesolve.com/blog/rpa-in-insurance-needs-intelligent-automation

[assistedge]

RPA or Robotic process automation in banking is the sure-fire strategy to reduce repetitive manual tasks and redirect resources to value-added services that align with the current market demand.

What is RPA & How Does it Help Banks and Other Financial Institutions? Banks and financial institutions are working in an extremely competitive environment, and quite rightfully so, to pursue the desired efficiency in catering to changing customer expectations. A tad bit of automation can help the former achieve its objectives and stay afloat with the top trends of 2022. These trends show that technology is the need of the hour, so is automation. Hence RPA is the missing piece of the competition.

Robotic Process Automation helps banks improve their processes, curtail unnecessary expenditures, and, most importantly, reimburse for various fraudulent transactions.

[maybeyoumissthisf-kingpost]

IT’S OUR GOVERNMENT, NOT TECHNOLOGY, TAKING OUR JOBS

I completely disagree with the view that the development of artificial intelligence (AI — Artificial Intelligence) will rob workers of jobs. Since the phrase “Industrial Revolution 4.0” has been repeatedly mentioned in recent years, more and more people fear it will cause widespread unemployment. This concern is not new. Many working-class protests demanding government control over artificial intelligence have taken place in many places; Even people with genius minds like Bill Gates, Mark Zuckerberg, Elon Musk, Stephen Hawking also warned about the “AI danger” to the labor market. But one thing needs to be clarified, it is the wrong policies of the government, not the automation mechanism, that takes away the jobs of the workers.

Not artificial intelligence, wrong government policy is the cause of unemployment

Uber is facing a lot of criticism in its project of developing a driverless car model because this model is “robbing jobs” of both traditional taxi drivers and Uber drivers. If only analyzing the tip of the problem, many people will immediately conclude that automation is increasing the unemployment rate in society; but if we analyze it more closely, we will see that this is the tip of an iceberg. Why does Uber have to invest billions of dollars to develop a self-driving car project when the wages for drivers, who are in the low-skilled workforce, are relatively cheap? Now, try to do some math and you will understand what we are talking about. Businesses when investing in a field always carefully consider the profitability of each project,So what has caused Uber and Amazon to spend billions of dollars looking for alternatives to workers? The root cause lies in the government’s regulations that make it difficult for businesses to “protect workers” that have adversely affected the industry. If governments continue to interfere in the labor market, in the future it will become cheaper to use artificial intelligence than to hire people to do the work.

Technology does not cause unemployment

Robots, digitization revolution, automation, artificial intelligence will not cause jobs to be lost, but will create a shift in the labor market. A rule that “experts” often deliberately ignore is that where there is demand for consumption, there is always work to do. Since the demand is infinite, the jobs will also be infinite.

The 21st century is an era of great technological advancements. Instead of supporting development, many people are worried and scared about the dominance of AI someday. The selfishness of a part of individuals and businesses is distorting the market, they conduct lobbying to call for protection from the government instead of finding ways to do better.

We often hear about ATMs taking jobs away from bank tellers, accounting software taking jobs from bookkeepers, e-commerce taking jobs away from employees. sales, etc. But according to the employment survey of the US Bureau of Labor Statistics (Bureau of Labor Statistics), the number of bank tellers, bookkeepers, and salespeople in 2009 was more compared with 1999. If technological developments are indeed reducing the number of jobs in these industries, why do the statistics show quite the opposite? With that said, the problem is not the technology, but how the government policy is.

Newspapers and movies are increasingly injecting people with fear of artificial intelligence, resulting in a wave of support for AI-related regulation, while forgetting about the offsetting benefits. ) that automation brings; they need a “visible hand” from government forgetting that government is not the solution to all our problems.

Compensating benefits of automation

During the 19th century, the weaver’s job was gradually replaced by machines, eliminating 98% of the labor needed to sew a piece of fabric. But let’s look at the other side of the problem, which is the reduction in the cost of fabric. The price of fabric decreases, which means that consumption will increase in accordance with the law of supply — demand. As the demand for fabric increases, the number of jobs in the textile industry increases.

If the above explanation is still not clear enough, then let’s look at the next example.

Take the case of ATM machines. ATMs perform many of the transactional functions of a bank teller, so the number of tellers in a branch becomes less. Operating costs for a branch office are now minimized. The cost of operating a transaction office will decrease, and the bank will open more branches to expand the market. More branches means more tellers need to be recruited, even if the number of tellers/branches decreases. Moreover, with the help of technology, tellers can do their jobs better and faster than before, the quality of service for customers will improve significantly. When customer value is added, it creates job growth.

In classical economics, we can explain this by Say’s law , or market law, that supply creates demand for itself. In other words, when technology improves resource efficiency, consumption increases.

Therefore, automation only creates displacement, not the loss of jobs. The most obvious shift is in the structure of the quality of the workforce, and the skills and technical qualifications of workers are increasingly being improved. It is a fact that workers living in countries with advanced technology always have a higher standard of living than workers in less technologically developed countries. At this point, everyone should be able to answer the question “Is technological development good or bad for the labor market?”.

Conclude

Around the world, there have been many studies showing that technology is the cause of unemployment, leading many people to view the achievements of the Industrial Revolution 4.0 as artificial intelligence, automation technology (RPA — t. Robotic Process Automation), cloud computing (Cloud Computing), virtual reality technology (VR — Virtual Reality), big data, etc. are more of a threat than a growth opportunity. Studies show changes in some structures, but not the picture of the whole market; so obviously the numbers they give are only half-truths, and half-truths are often big lies. Technology is not our economic enemy; on the other hand, it is also necessary to create new job opportunities, thereby improving the quality of life. If the unemployment rate changes,what we need to consider is “what did the government just do?”, “what policy did they enact?”, not blaming technology. For example, the relaxation of business controls under Donald Trump has brought the unemployment rate in the US down to 3.8%, a 50-year record low; this is a good example of how government policy is what affects the unemployment rate. The biggest impact of technology on workers is probably increasing their productivity, so workers need to always improve their technical skills to keep up with changes in the times. new. That way, the support from technology will help bring us the best products, services and goods ever.For example, the relaxation of business controls under Donald Trump has brought the unemployment rate in the US down to 3.8%, a 50-year record low; this is a good example of how government policy is what affects the unemployment rate. The biggest impact of technology on workers is probably increasing their productivity, so workers need to always improve their technical skills to keep up with changes in the times. new. That way, the support from technology will help bring us the best products, services and goods ever.For example, the relaxation of business controls under Donald Trump has brought the unemployment rate in the US down to 3.8%, a 50-year record low; this is a good example of how government policy is what affects the unemployment rate. The biggest impact of technology on workers is probably increasing their productivity, so workers need to always improve their technical skills to keep up with changes in the times. new. That way, the support from technology will help bring us the best products, services and goods ever.this is a good example of how government policy is what affects the unemployment rate. The biggest impact of technology on workers is probably increasing their productivity, so workers need to always improve their technical skills to keep up with changes in the times. new. That way, the support from technology will help bring us the best products, services and goods ever.this is a good example of how government policy is what affects the unemployment rate. The biggest impact of technology on workers is probably increasing their productivity, so workers need to always improve their technical skills to keep up with changes in the times. new. That way, the support from technology will help bring us the best products, services and goods ever.

All credit goes to trantuansang.com.

[ellicium]

In today's digital age, the rise in ATM crimes poses a significant challenge for banks and financial institutions worldwide. As the number of fraudulent transactions continues to escalate, bank employees are faced with the daunting task of processing a growing number of applications, all while ensuring the safety and security of their customers' funds. Our expert team harnessed the power of hashtag#RPA to streamline and automate the process for bank card fraud. Let's unlock the full potential of automation for your organization. Download our case study today and discover how Ellicium Solutions Inc. RPA solutions can transform your business operations: https://ellicium.com/services/robotic-process-automation/  

[adityarana1687-blog]

Robotic Process Automation in BFSI Market Expected To Achieve Lucrative Growth By 2025

The global robotic process automation in BFSI market size is expected to reach USD 1,123.0 million by 2025, and it is anticipated to grow at CAGR of 31.3% from 2019 to 2025, according to a new report by Grand View Research, Inc. The introduction of Digital 2.0 is driving digital transformation in the banking and financial services sector. Some of the key technologies used in the banking, financial services and insurance (BFSI) sector include Artificial Intelligence (AI) & machine learning, robotic process automation, blockchain, Internet of Things (IoT), and open banking platforms. The growing need to provide enhanced user experience and meet the needs & expectations of customers is leading to a surge in digital banking.

Banking and financial services are highly competitive markets. The BFSI sector requires documents for various banking processes, along with numerous legacy systems for paperwork. For instance, though loan applications can be processed online, supplementary documents and other pieces of information still require manual intervention. In such cases, robotic process automation bots can work across different legacy systems to retrieve information available on digital platforms. These automation solutions cater to two distinct categories; attended & unattended robotic process automation (RPA). The attended RPA is useful in automation of tasks that require human intervention, such as front-end tasks. On the contrary, the unattended RPA performs repetitive and deterministic functions in the back end.

Robotic process automation has potentially benefited the banking sector by expediting individual case handling processes and meeting regulatory compliances. In the insurance sector, RPA has enabled a reduction in time spent on inbound calls by digitizing the process, thereby optimizing the turnarounds. The financial industry handles a large number of complex manual processes and often faces issues owing to human error. In such scenarios, robotic process automation has enabled organizations to eliminate manual errors while improving the overall process quality.

The banking sector is one of the largest consumers of IT services and products. Various industries across the globe have been influenced by digital transformation. The banking sector has also increased its IT spending significantly over the past few years. Although the level of investment has increased, where new services and products are being introduced continuously to automate banking processes, there is still a considerable amount of repetitive and manual work that continues to drive down productivity. Robotic process automation offers flexibility, is easy to implement, and has a shorter payback period. These advantages make it a better alternative as compared to traditional IT solutions. However, there is no standard RPA solution available to address the needs of every sector. Companies experiment with the potential of these solutions and optimize robotic process automation accordingly to gain a competitive edge.

To request a sample copy or view summary of this report, click the link below: www.grandviewresearch.com/industry-analysis/robotic-process-automation-bfsi-market

Further key findings from the report suggest:

The robotic process automation in BFSI market was valued at USD 167.1 million in 2018 and is projected to register a CAGR of 31.3% over the forecast period

The services segment dominated the market in 2018 owing to the fact that RPA services are enabling a seamless transition from legacy systems

The training services segment is anticipated to register the highest CAGR over the forecast period

North America region dominated the market in 2018 with over 44% of the overall market share

The prominent market players include Blue Prism; Automation Anywhere Inc.; Kofax Inc.; and Kryon Systems, among others.

Grand View Research has segmented the global robotic process automation in BFSImarket on the basis of type, service, organization, application, and region:

RPA in BFSI Type Outlook (Revenue, USD Million, 2015 - 2025)

Software

Services

RPA in BFSI Service Outlook (Revenue, USD Million, 2015 - 2025)

Consulting

Implementation

Training

RPA in BFSI Organization Outlook (Revenue, USD Million, 2015 - 2025)

SMEs

Large Enterprises

RPA in BFSI Application Outlook (Revenue, USD Million, 2015 - 2025)

Banking

Financial Services & Insurance

RPA in BFSI Regional Outlook (Revenue, USD Million, 2015 - 2025)

North America

Europe

Asia Pacific

Latin America

Middle East & Africa

U.S.

Canada

U.K.

Germany

France

China

India

Japan

Brazil

Mexico

About Grand View Research

Grand View Research, Inc. is a U.S. based market research and consulting company, registered in the State of California and headquartered in San Francisco. The company provides syndicated research reports, customized research reports, and consulting services. To help clients make informed business decisions, we offer market intelligence studies ensuring relevant and fact-based research across a range of industries, from technology to chemicals, materials and healthcare.

[neuvay]

BENEFITS OF ARTIFICIAL INTELLIGENCE AND AUTOMATION IN THE WORKPLACE

In general term, Artificial Intelligence refers to a machine which mimics human cognition. Artificial Intelligence (AI) is a transformation in itself, and it is crippling fast into all facet of our lives and across industries. The fact remains that every company needs an AI-based application that will help them sustain their business model for the future generation.

In recent times, B2B and B2C businesses have recorded great return-on-investment through AI and automation of business processes. Gradually, organizations are becoming more smarter; decision makers now have deep business insights and employees empowered through the adoption of AI and Robotic Process Automation (RPA) at the workplace.

However, Artificial Intelligence is not a substitute for the workforce, but rather, an enabler to make employees smarter and allow time for other productive tasks.

Why Artificial Intelligence in the Workplace?

Whether AI is being used for business analysis in companies, or to guide financial advisors at investment companies, artificial intelligence is rapidly re-defining the workplace. And human resources departments should ensure employees are ready for the change through the acquisition of in-demand skills. Sooner, bots will be able to execute more of the task done by humans, especially repetitive ones, the work that humans do, and even perform some roles that go beyond what humans can do. As a result, some occupations will decline, others will grow, and many more will be altered.

Recent research reveals that over 2000 work activities across more than 800 occupations show that certain categories of activities are more easily automatable than others. These include physical activities in highly predictable and structured environments, as well as data collection and data processing. This account for roughly half of the activities that people do across all sectors. The least susceptible categories include managing others, providing expertise, and interfacing with stakeholders. AI and workplace automation will have a positive effect on companies’ growth and revenue generation like never before. This new technology will make work easier for employees, streamline business processes, increase efficiency, and reduce workplace hazard.

Let’s analyze how AI and Automation are helping some industries maximize profits and increase efficiency:

How Artificial Intelligence is revamping Retail and Fashion World

Often, AI and automation discussion have always been around fear of losing jobs. However, AI, otherwise called Machine Learning is enhancing control, improving creativity, and will bring faster innovation to the retail and fashion sector. Automation is helping fashion pro’s to be better at what they do. For instance, it has made online fashion retail possible which has unimaginable scope to speed up visual merchandising by applying large data sets to forecast and understands patterns and subsequently making recommendations that shoppers want. As an organization, having limited or excess supply of goods can affect your company’s bottom line. With AI, restocking is made simple. Artificial Intelligence and automation enable retailers to replenish supplies by identifying products or goods that are in high demand as a result of location, sales history among others. It would help organizations predict what customers want, stock the right product-just in time as per seasonal consumer behaviors, increase revenue, and product returns. Aside from getting insights about the stock, retail companies can also leverage AI to predict results of various pricing strategies for best promotional offers, increase sales and turn leads to customers faster.

Financial Institutions and RPA

We often talked about Artificial Intelligence and automation concerning futuristic dystopian visions, but their near-term usefulness is evident in the banking industry. Since data is an important component of Artificial Intelligence (AI), it is not a surprise that financial institutions were among the first to adopt Artificial Intelligence (AI) and they already see the return-on-investment. Increase in Robotic Process Automation (Rpa automation anywhere) has made banks to automate repetitive clerical tasks such as requests for replacement of credit cards and loan applications. Trained bots in the banking sector can execute tasks within a limited time, with higher accuracy and at a reduced cost than would be the case with people involved, allowing financial institutions to cut down on back-office function overheads.

Artificial Intelligence in Medical Field

The health sector is currently enjoying the benefits of AI and automation. One of the challenges facing the health sector is the inability of doctors and healthcare regulators to attain perfection. Research shows that a whopping improving diagnosis in health care and several hospital complications are the result of diagnostic errors. A study shows that combining human effort with automated tools can raise the diagnostic rate to 97 percent or higher. Artificial Intelligence systems are designed to empower medical doctors to provide a higher level of care. These AI systems can ensure compliance in every step of the process.

There are now online medical applications which have been developed via Artificial Intelligence so that the sick can get health advice on the go in the absence of a medical doctor. Once you observe any strange body symptoms or reactions, you can log into the app if you have a membership account and you will get timely health advice and information anywhere you are.

Bots for the Energy Industry

The energy sector is not left out in the adoption of AI and Robotic Process Automation. In our quest for sustainable green energy for the safety of lives, bots are helping the energy sector to store energy, manage renewables, and optimize resources.

Through AI, we are gradually getting to a point where energy is neither created nor consumed centrally. Energy “prosumers” will connect their distributed resources to the grid, downloading and uploading it according to need, with potential payment for their surplus supply. AI algorithms will, for instance, decode patterns of behavior on, say, a weekday evening in 2025, when drivers arrive home and put their cars on charge. The AI will distinguish between drivers who use their vehicles overnight and those who leave cars charging until the following morning; the intelligent grid will ensure that the battery charged in time for the driver’s next journey, without mounting continuous load on the network.

The Author is an IT thought leader in the retail and fashion world and has written this article for a new division “NeuVays” of Mckinsol, focused on emerging technologies.

NeuVays is dedicated to developing products based on Blockchain, AI, ML and IoT at McKinsol. NeuVays is aggressively hiring the right caliber of people in Asia at their offshore development office.

[dvtsa46]

Sharing Our Studying On Robotic Process Automation

The capability to do more with much less keeps earnings up with out incurring costs. The enterprise can operate extra efficiently, which paves the method in which for enterprise progress. In order to offer robotic process automation you the content material requested, we have to store and process your persdonal information. If you consent to us storing your persdonal data for this function, please tick the checkbox beneath.

RPA allows us to faux that a human worker is performing duties on a computer. This could possibly be a Windows programme , an API or programming interface or maybe a webpage/web software. In many instances, using RPA contributes to elevated visibility of assets and the identification of process gaps and areas the place improvements are still lacking. Robotic Process Automation is a useful rpa services and efficient approach to automate frequent and repetitive IT-underpinned tasks. It is as a lot as you the place you start your automation journey – will or not it's large scale or small? Again, discovery workshops or trials could additionally be the easiest way to grasp the path you need to go.

For absolutely automated workflows, controls and gear can present an finish to finish resolution. However, in many cases it's advantageous to contain human workers to supervise the sleek working of the machinery, and to supply visual quality checks in addition to automated inspection. The role of RPA is to complete the packaging workflow with automated processes which pick and place goods in the secondary packaging, and to automate sealing and labelling. Finished packs usually exit the packing line by conveyors and are positioned on pallets by robotic arms or manually. Stacked pallets can then be wrapped to type a single, safe unit prepared for transport or storage.

The use of computerised instruments also provides full knowledge recording of the operation for monitoring, evaluation, reporting and costing functions. This democratization of know-how by way of low-code and no-code tools is one thing we're enthusiastic champions of right here at Nintex. Digital transformation is a journey that should be embraced throughout the organization. This is particularly the case with process automation, since it’s the folks themselves that basically know which processes work nicely and which have to be improved. By giving teams the flexibility to contribute to process improvement, you empower your workforce to make the change they want to see on the group, without having to rely upon IT.

RPA is an effective solution for processes that see irregular levels of demand as they can simply scale up or down. This can take away the business need to flex the variety of people robotic process automation services wanted to complete an activity. The greatest candidates for RPA are stable processes that are high volume, rule-based, and possess each structured inputs and outputs.

We have a big pool of licensed RPA specialists which have labored across industries and geographies to ship successful RPA-led digital transformation initiatives. This helps us to transfer hands-on RPA data to our shoppers in an accelerated method. As a result, RPA use instances unfold across multiple sectors, including banking, healthcare, and telecom, and business features, such as RPA in HR and accounting.

Robotics is also best for managing workload peaks, without hiring short-term employees or re-deploying staff who don’t perceive the work & are needed elsewhere. We convey digital products from validation to success and teach you ways because we care. It’s time to move past simply constructing features and begin designing the proper product with the right technique.

While RPA works well for simple, well-defined duties, it lacks the intelligence and suppleness required to automate increasingly complex processes. Additionally, with RPA, it’s simple to become burdened with technical debt as expensive implementations and maintenance services require the continuous deployment of more bots to account for extended scale. The pandemic highlighted the need of coordinating automation across a enterprise. GlobalData estimates that the global RPA market might be value $20 bn by 2030. A financial institution with one hundred branches and one thousand staff previously did most processes manually.

We’ve advanced a best-practice approach designed to ensure the most applicable RPA deployment strategy for your organisation, primarily based on the premise that every organisation is totally different. We allow you to build the enterprise case for RPA, identifying one of the best operate, group or unit for a proof of idea then help you execute it. Extra Technology’s staff of RPA specialists have all labored on profitable RPA deployments in giant enterprise organisations and are accredited to the highest possible stage. Get a free and personalised quote for your corporation by completing the shape beneath. Our expert support team will name you inside 24 hours to debate your requirements. From receipt of goods to the creation of an accounting e-book entry, Esker’s P2P resolution permits businesses to automate each section of the P2P cycle and positively rework the finest way they buy, book and pay.

RPA can dramatically improve processing speeds, boosting efficiency, productiveness and accuracy as human error/mis-keying is eliminated. Robot programming is quickly changing into a sizzling topic as more companies show interest in and invest in robots. This Robot Framework training robotic automation process course will present delegates with elaborated information about the making a control plan for a way a machine interacts with its surroundings and achieves its objectives. Robotic process automation can be utilized to streamline every of those activities.