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How Cybersecurity Services Protect Businesses in the Digital Age

Today, digital transformation is the new business strategy; no organization can afford not to pay attention to cybersecurity services. Dependence on cybersecurity services is inevitable due to the daily rise in cyber threats based on their complexity and frequency. Hence, sensitive data and digital assets must be well protected. This blog post will discuss the diverse aspects of cybersecurity, such as cybersecurity management, cybersecurity operations centers, and the role of cybersecurity professionals.

Financial loss is the largest risk associated with cybersecurity. According to statistics and trends, cyberattacks are one of the most expensive business hazards. In 2023, an astounding $8 trillion was lost by businesses globally due to cybercrime; by 2027, that amount is predicted to increase to almost $24 trillion.  According to Cybersecurity Ventures, cybercrime is the most significant transfer of wealth in history.

Cybercrimes for small and medium organizations

It is simple to believe that large, publicly traded corporations are cybercriminals’ primary targets. However, small and medium-sized business (SMB) attacks are increasing, and smaller organizations are particularly vulnerable to several types of cyberattacks. Small firms are especially vulnerable to cyberattacks, disrupting regular operations and destroying crucial IT infrastructure or assets. Without the funds or means to recover from this devastation, it may be hard to do so. Many smaller businesses cannot afford the high cost of maintaining cybersecurity.

Nearly half of cyberattacks affect small organizations

Small firms are by no means immune to cyber risks, as we have already discussed. Companies with fewer than 1,000 employees are impacted by 46% of all cyberattacks globally.

41% of small organizations experienced cyber attacks

Over 40% of small firms reported having experienced a cyberattack in 2023.

Small organizations are more likely to be targeted through phishing attacks

Types of Cyber Attacks on the Rise in 2025

Cybercriminals are constantly developing new ways to get around better security measures and demand more money and data from businesses. However, what kinds of cyberattacks are most likely to occur in 2025?

Malware

Malware is one of the most prevalent cybersecurity threats, with over 1.2 billion programs in use today. Any harmful computer software that can harm computer systems and aid hackers is called malware. Although fewer malware programs were found in 2024, the threat is still very much present.

Ransomware

A particular kind of ransomware malware seeks to extort money from companies to unlock the files and data stored on their systems. This cyber threat is one of the most expensive cyberattacks for companies and is a type of extortion. Attacks using ransomware have been increasing; in 2023, they increased by about 67%.

Phishing and Social Engineering

Social engineering is another prevalent form of cyberattack that targets companies, as thieves gain staff members’ trust before manipulating them. Phishing is the most common social engineering assault. Phishing criminals may pose as respectable businesses and send emails and messages to staff members to get passwords, bank account numbers, and other private information. Z Scaler reports that phishing attempts increased by 58.2% in 2023, with the finance sector being the most often targeted industry.

Who’s Behind the Data Breaches?

Importance of Cyber Security Services

Cybersecurity is an applied science that guards’ information systems and networks that empower electronic communication. The discipline combines scientific facts and creative problem-solving as practitioners strive to predict and fight against changing dangers. Cybersecurity specialists provide consulting, engineering, and IT security services to secure businesses from hostile people and attacks by nation-states on their systems and infrastructure.

Key Elements of Cyber Security Services:

Cyber Security Management

Cybersecurity management is developing a strategy involving risk assessment, policy formulation, and incident response planning. It entails a holistic approach to identifying an organization’s infrastructure vulnerabilities and implementing appropriate measures to mitigate risks.

Risk Assessment: This will identify the different threats to an organization’s assets and evaluate them in terms of likelihood and potential impact. In this way, organizations can identify emerging threats on time.

Policy Development: In detail, clear policy development on data protection, access for users, and response in case of incidents as one of the major key enablers is setting an environment to be secure. These help guide employees to best practices and set expectations for compliance.

Incident Response Planning: An effective incident response plan consists of the steps that organizations should undertake when there is a security breach. This plan allows organizations to respond quickly by minimizing damage and recovering efficiently.

CSOC

A Cyber Security Operations Center is a centralized unit responsible for monitoring, detecting, and responding to security incidents in real-time. It plays an important role in maintaining an organization’s security posture by providing continuous surveillance and analysis.

Monitoring: It identifies suspicious activities that evolve into massive incidents if unidentified or unreported early.

Threat intelligence: It analyses data through various methods and correlates the findings using threat intelligence tools.

Incident Response: It coordinates incident response efforts during a security incident. This coordination ensures that necessary actions are undertaken quickly to limit damage.

Why managed security services matter?

Today, organizations depend entirely on technology, and many more choose managed security service providers like ESDS for better cybersecurity services. ESDS provides different types of managed security services to protect various organization data from emerging cyberattacks.

Advantages of ESDS Managed Security Services:

Cost-Effectiveness: – Thus, organizations incur huge costs and take years to build their in-house cyber security. Using services provided by  ESDS Managed Security Services can save a lot of money.

Scalability: offers scalable solutions that adapt to any organization to ensure protection during growth.

Compliance with Statutory and Regulatory Compliance: ESDS ensures clients adhere to all applicable laws and regulations by setting the proper controls and conducting regular audits.

Conclusion

Cybersecurity services have become a more prominent way to ensure the security of sensitive data and the trust of customers and other stakeholders in today’s digital landscape. Key components like cybersecurity management and cybersecurity operations help build a strong shield against cyber threats.

Additionally, the ESDS can assess the organization’s ability to effectively manage its cyber security needs.

Let us know in the comment section below regarding the usefulness of the cyber security services.

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The Rise of Explainable AI: Building Trust and Transparency

Artificial intelligence is fast changing the business landscape, becoming deeply embedded into organizational processes and daily life for customers. With this speed, however, comes the challenge of responsible deployment of AI to minimize risks and ensure ethical use.

One of the fundamental pillars of responsible AI is transparency. AI systems, comprising algorithms and data sources, must be understandable, enabling us to understand how decisions are made. This transparency ensures that AI operates fairly, without bias, and in an ethical manner.

There have been worrying cases where AI’s use has remained opaque, while more than many companies are performing quite well in terms of transparent AI. The lack of clarity has the potential to erode trust, and things get serious for businesses and their customers.

This blog explores some real-world examples of how transparent AI has been well used, and its absence has led to problems.

What Is AI Transparency?

AI transparency refers to making AI systems interpretable, auditable, and accountable. The information on how an AI system works, what data it uses, and the logic behind its decision-making process are all shared under this principle.

Transparency ensures that stakeholders—developers, end-users, and regulators—can scrutinize the AI’s processes, enabling trust and reducing the risks of biased or unethical outcomes.

Transparent AI systems answer key questions such as:

What data is the AI system trained on?

How are decisions made?

Are biases being mitigated?

By addressing these questions, AI transparency provides the clarity needed to build systems that are fair, reliable, and safe.

Misconceptions About AI Transparency

Although AI transparency is very important, there are many misconceptions about it.

Transparency Equals Full Disclosure

Many people think that AI transparency requires every detail about an AI system’s functioning. But in actuality, such broad disclosure is not always practical or necessary. Transparency actually focuses on making systems understandable without drowning stakeholders in unnecessary technical complexities.

Transparency Is Only About the Algorithm

Transparency is not limited to disclosing the algorithm. It also includes data sources, model training processes, decision-making logic, and system limitations.

Transparency Equals Vulnerability

Some organizations believe that transparency about the artificial intelligence system renders it vulnerable or compromises the trade secret of the company. However, one can share partial information if they are trying to balance safeguarding intellectual property while being transparent.

Transparency Automatically Solves Bias

Transparency is a tool, not a solution. While it helps identify biases, eliminating them requires proactive measures like data cleansing and continuous monitoring.

Why is AI transparency important?

There is growing dependency on AI that requires increased levels of transparency; this has importance for various reasons:

Building Trust

Users and other stakeholders would develop trust for an AI system more readily when the decision-making mechanism is comprehensible. Thus, transparency “black boxes” makes AI nonthreatening and more believable.

Responsibility Building

Transparent systems allow organizations to identify accountability, especially when AI decisions lead to unintended consequences. This accountability promotes a culture of responsibility and ethical practices.

Bias Detection and Elimination

Transparency will help to reveal biases in data or algorithms so that developers can address these issues before they impact decision-making.

Facilitating Regulation Compliance

With regulatory frameworks like the EU AI Act, transparent AI systems are essential for meeting legal requirements and avoiding penalties.

Improving AI Performance

Transparency encourages continuous improvement. By identifying weaknesses in AI models, organizations can refine them for better performance and accuracy.

GenAI Complicates Transparency

The rise of generative AI (GenAI), which creates content like text, images, and videos, adds new challenges to achieving AI transparency.

GenAI systems, such as OpenAI’s GPT models or Google’s Imagen, are inherently complex. Their reliance on vast datasets and intricate neural networks makes understanding their outputs more difficult. For example:

Training Data Opaqueness: GenAI models are often trained on massive datasets, which may include copyrighted, biased, or sensitive material. Lack of clarity around these datasets leads to ethical and legal concerns.

Unpredictable Outputs: GenAI systems produce outputs based on probabilistic patterns, making it harder to predict or explain specific results.

To address these challenges, organizations must develop specialized frameworks for ensuring transparency in GenAI systems.

Transparency vs. Explainability vs. Interpretability vs. Data Governance

AI transparency is often confused with related concepts, explainability, interpretability, and data governance. While undoubtedly they are related, each has a different meaning:

Transparency: It means making the design, operation, and decision-making of an AI system clear.

Explainability: The capacity to explain why a particular AI decision was taken. It is a subset of transparency, with an emphasis on outcomes rather than the system.

Interpretability: refers to the explanation of how inputs and outputs in an AI model are interlinked. This is more technical and is an explanation of how a model works from within.

Data Governance: Here, policies and practices would be included to ensure that data used in AI systems is correct, secure, and in compliance with regulations.

Together, these articles form a rich framework for responsible AI development and deployment.

Techniques for Achieving AI Transparency

Organizations can adopt several techniques to enhance AI transparency:

Model Explainability Tools

Tools such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) enable developers to know how an AI model made its decision.

Data Lineage Tracking

The proper maintenance of records on the source of data, transformation, and usage ensures traceability and accountability.

Human-in-the-Loop (HITL) Systems

Including human beings in important decision-making will add more responsibility and less reliance on complete automated systems.

Algorithm Audits

Regular audits of algorithms ensure they align with ethical and regulatory standards.

Transparency Documentation

Creating comprehensive documentation for AI systems, including training data, model architecture, and known limitations, promotes clarity and trust.

Regulation Requirements for AI Transparency

Various governments and regulatory bodies worldwide are now proposing frameworks that enforce transparency in AI. Examples include:

EU AI Act

The EU’s proposed AI Act sets an obligation for high-risk AI systems to be explainable and transparent, that is, understandable to users in their operation and limitations.

US AI Bill of Rights

The White House’s framework gives principles on the ethical use of AI and transparency in automated decision-making.

Global AI Governance

Initiatives like the UNESCO AI Ethics Recommendation demand cooperation at the global level to formulate standards of transparency and accountability. Compliance with these regulations is not only a legal requirement but also a strategic advantage in building customer trust and avoiding reputational damage.

Conclusion

The transparency that AI is able to maintain at this stage and in these ages of advanced pervasive AI technologies no longer stands as a choice but an imperative for achieving trust, liability, and morally responsible AI.

While challenges such as the complexity of GenAI systems and misconceptions about transparency still exist, being proactive through approaches such as the use of explainability tools, algorithm audits, and transparency documentation can pave the way for success.

The evolving regulatory framework will benefit those organizations that embrace transparency in this ever-changing AI landscape. Through embracing transparency, we ensure that AI works for the good, promoting innovation and protecting the ethics while inspiring trust in this revolutionary technology.

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Master DRaaS in 2025: Top 10 Best Practices for Success

“What if a disaster struck your business right now? Could you afford hours of downtime or the loss of critical data?”

The need for strong disaster recovery solutions has never been higher in 2025, nor has the dependence on technology. Disaster Recovery as a Service (DRaaS) has emerged as a vital security measure for businesses in every industry as the digital landscape changes. The stakes are high because, according to a Gartner study, the average cost of IT outages can reach over $10,000 per minute, and companies frequently find it challenging to recover in the absence of a strong disaster recovery plan.

When properly deployed, DRaaS not only reduces the financial risk of downtime but also guarantees that your company can bounce back fast, keep customers’ trust, and continue to run even during emergencies. In this blog, we’ll walk you through the top 10 best practices for deploying DRaaS effectively in 2025, ensuring your organization remains resilient in the face of unforeseen challenges.

1. Conduct a thorough risk assessment

First of all, any attempt at implementing the disaster recovery service has to assess potential risks or vulnerabilities, identifying a broad view of disasters; it must entail scenarios like:

Cyberattacks (e.g., ransomware, DDoS attacks)

Natural disasters (floods, earthquakes, etc.)

System failures (server crashes, data corruption, etc.)

This analysis helps you establish well-defined Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO), which specify how fast you must recover after a disruption and how much data you can afford to lose. In 2025, increasing cyberattacks and global environmental changes make comprehensive risk management more essential than ever.

2. Select the Suitable DRaaS Service Provider

The choice of disaster recovery as a service provider can make or break your recovery strategy. A reliable provider offers:

Geographic Redundancy: Simply put, having multiple data centres in various geographical locations ensures that if disaster strikes in one region, your data would never be compromised.

Automation and Orchestration: Automated failover and failback processes prevent human error in the recovery process, thus speeding up the time to recovery.

Compliance: Your DRaaS provider must fulfill all of the required compliances, including GDPR, HIPAA, or PCI DSS, for a business that operates in a highly regulated sector.

3. Utilize Cloud-Based Disaster Recovery Services

Cloud-based disaster recovery in cloud computing is a game-changer for businesses in 2025. It provides several advantages over traditional on-premises disaster recovery solutions:

Scalability: Cloud DRaaS solutions scale up or down based on your business needs, without the need for significant upfront investment.

Cost Efficiency: Instead of maintaining a secondary data center for DR, cloud DRaaS eliminates this capital expenditure, as you only pay for what you use.

Real-Time Replication: Cloud-based solutions offer continuous data replication, ensuring that the most recent data is available for recovery.

Recent reports show that 95% of enterprises are shifting their disaster recovery strategies to cloud-based platforms, signaling the growing confidence in the cloud’s reliability and scalability.

4. Integrate Strong Cybersecurity Protocols

As cyberattacks continue to evolve, integrating advanced cybersecurity measures into your disaster recovery solution is non-negotiable. Incorporate the following best practices:

Encryption: Ensure that data is encrypted at rest and in transit, preventing unauthorized access during the recovery process.

Immutable Backups: Immutable backups are essential to safeguard against ransomware attacks. These backups cannot be altered, ensuring that your data remains untouched.

Multi-Factor Authentication (MFA): Use MFA to protect access to recovery systems, adding an extra layer of security to prevent unauthorized interventions.

5. Regularly Test and Validate Your DRaaS Plan

Testing your DRaaS setup is critical to ensuring its reliability during a real disaster. Failover drills should be performed regularly to assess recovery times and the integrity of your backups. Ensure that:

Application Consistency: All applications and databases are consistent across backup systems and the recovery environment mirrors production.

Team Familiarity: Your team is well-versed in the recovery process and their roles during a disaster.

Testing your plan ensures that when a disaster strikes, the recovery process is seamless, efficient, and ready to go live.

6. Automate Procedures for Failover and Failback

Automation is one of the best ways to reduce human error and speed up recovery. Automating failback (the process of returning to the primary system after recovery) and failover (the process of switching to a backup system in case of failure) streamlines the recovery process and removes the need for manual intervention.

Using disaster recovery orchestration tools, businesses can design workflows that, for example, initiate the recovery process automatically upon detection of a disaster without requiring human input.

7. Optimize for Cost Efficiency

While disaster recovery services are a necessity, they can be expensive if not optimized. Some of the cost-efficient strategies are as follows:

Tiered Backups: Keep less important data in less expensive, long-term storage and important data in high-performance, low-latency storage.

Pay-As-You-Go Models: Choose a DRaaS provider that offers a flexible pricing model based on usage. You’ll only pay for disaster recovery services when a disaster occurs, reducing costs during normal operation.

Research finds that organizations reduce their disaster recovery costs by about 40 percent when they implement a cloud model based on the pay-per-use practice.

8. Ensure Application Consistency

Data consistency across applications is vital to ensuring a smooth recovery process. If your application data is out of sync, it could lead to errors or downtime. To achieve application consistency:

Real-Time Replication: Implement real-time data replication across backup servers to keep application data synchronized.

9. Empower Your Team with Training and Awareness

Having the best technology is only half the battle. Your disaster recovery plan will fail if your team isn’t prepared. Empower your IT staff and business users by:

Providing DRaaS Training: Regularly train your team on how to execute disaster recovery plans and troubleshoot potential issues.

Clear Communication Channels: Develop a communication strategy that keeps key stakeholders informed during a disaster recovery event.

Regular training will help your team respond more efficiently during an actual disaster, ensuring a faster recovery.

10. Constantly Track and Improve

The needs for disaster recovery, along with the IT industry, are always changing. Maintaining the efficacy of your DRaaS solution requires constant monitoring and optimization.

Real-Time Monitoring: To keep tabs on the functionality and condition of your disaster recovery infrastructure, use monitoring tools.

Performance Metrics: Make sure RTO and RPO are meeting the needs of your company by regularly evaluating them.

Updates to the infrastructure: Update your disaster recovery plan as your business grows to account for changes in technology and business procedures.

As cloud technologies advance quickly, DRaaS solutions must also evolve to meet the changing needs of your business.

We at ESDS have extensive experience in providing highly reliable and cost-effective disaster recovery as a service tailored specifically to the needs of businesses in 2025. Our DRaaS solutions offer:

Seamless Cloud Integration: Scalable, secure, and efficient disaster recovery solutions that can seamlessly integrate with all cloud platforms.

Advanced Security Features: Immutable backups, end-to-end encryption, and AI-driven threat detection to secure data in case of recovery.

24/7 Support: Our experts are always there to help you in case of a disaster, so the business is back to normal in the shortest possible time.

Discover how ESDS DRaaS services protect your business today against the unforeseen disruptions of tomorrow. Let’s talk.

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How Artificial Intelligence (AI) is Transforming Traditional IT Services

Artificial Intelligence (AI) is revolutionizing traditional IT services by introducing automation, predictive capabilities, and enhanced decision-making. It empowers IT teams to improve efficiency, optimize processes, and deliver superior user experiences.

Key Transformations Brought by AI

Automation of Repetitive Tasks

AI automates routine IT tasks like system monitoring, patch management, software updates, and backups.

Tools such as RPA (Robotic Process Automation) reduce manual effort, allowing IT teams to focus on strategic activities.

Enhanced IT Security

AI-based systems use machine learning to detect anomalies and potential cyber threats in real-time.

Predictive analytics can identify vulnerabilities and respond to incidents faster, reducing risks.

Example: AI-powered threat detection in Security Operations Centers (SOCs).

Predictive Maintenance

AI analyzes system performance data to predict hardware or software failures before they occur.

This minimizes downtime, optimizes resource usage, and improves overall system reliability.

AI-Powered IT Support (Chatbots and Virtual Assistants)

AI chatbots provide 24/7 support, resolving common issues like password resets or connectivity problems.

Virtual assistants help IT staff by streamlining workflows and quickly retrieving necessary data.

Smarter Resource Management

AI helps allocate IT resources (compute, storage, and bandwidth) dynamically based on usage patterns.

Intelligent algorithms optimize cloud usage, reducing costs and improving scalability.

Intelligent Monitoring and Incident Management

AI-driven IT monitoring tools proactively detect and resolve issues before users notice them.

Example: AIOps (Artificial Intelligence for IT Operations) platforms use machine learning to analyze logs, identify patterns, and recommend fixes.

Data-Driven Decision Making

AI systems analyze vast amounts of data generated by IT environments to provide actionable insights.

This aids IT leaders in making informed decisions about infrastructure upgrades, cost optimization, and innovation.

Improved User Experience

AI enhances IT service management (ITSM) by personalizing user experiences and accelerating ticket resolution.

Natural Language Processing (NLP) in chatbots improves communication with non-technical users.

Edge Computing and IoT Management

AI manages complex IT setups involving edge devices and IoT networks, ensuring low latency and high performance.

It processes and analyzes data closer to the source, improving efficiency in real-time applications.

Software Development and Testing

AI accelerates application development with code generation tools and automates testing processes.

It identifies coding errors and potential vulnerabilities, reducing time-to-market.

Benefits of AI in IT Services

Efficiency Gains: Reduced manual intervention and faster issue resolution.

Cost Savings: Optimized resource usage and reduced operational overheads.

Proactive Approach: Shift from reactive to proactive problem-solving.

Scalability: Enhanced ability to manage growing IT environments seamlessly.

Conclusion

AI is transforming traditional IT servicesinto more dynamic, intelligent, and efficient systems. By automating tasks, enhancing security, and providing predictive insights, AI enables IT teams to meet modern business demands while driving innovation and reducing operational burdens.

Related Blogs

Unlocking the Potential of AI-Based Technology

The Future of AI is here: AI-Enabled Authentication Processes

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What exactly is Core Banking?

From the last 3-4 years, we are reading news like ‘XYZ bank deploys Core Banking Solutions ’,’ Bank ABC is implementing Core Banking System ’ etc.  But what exactly core banking is?

Core Banking Solution (CBS) is the networking of bank branches, which allows customers to manage their accounts, and use various banking facilities from any part of the world.

In simple terms, there is no need to visit your own branch to do banking transactions. You can do it from any location, any time. You can enjoy banking services from any branch of the bank which is on the CBS network regardless of the branch you have opened your account.

For the bank which implements CBS, the customer becomes the bank’s customer instead of the customer of a particular branch.

Execution of Core banking system across all branches helps to speed up most of the common transactions of bank and customer. In Core banking, all branches access banking applications from a centralized server which is hosted in a secured datacenter.

Banking software/application performs basic operations like maintaining transactions, the balance of withdrawal & payment, interest calculations on deposits & loans, etc. This banking application is deployed on a centralized server & can be accessed using the internet from any location.

Why we need Core Banking Technology ?

Nowadays, the use of Information Technology (IT) is a must for the survival & growth of any organization and the same applies to the banking industry also. By using IT in any industry, banks can minimize the operation cost; also banks can offer products & services to customers at competitive rates.

CBS is required

To meet the dynamically changing market & customer needs.

To improve & simplify banking processes so that bank staff can focus on sales & marketing stuff.

Convenience to customers as well as banks.

To  Speed up the banking transactions.

To expand presence in rural & remote areas.

Basic elements of CBS that helps customers are

Internet Banking

Mobile Banking

ATM

POS & kiosk systems

Fund Transfers – NEFT, RTGS

Benefits of Core banking

Core banking solutions are beneficial to both banks as well as customers.

A] Benefits For Customers

Quicker services at the bank counters for routine transactions like cash deposits, withdrawal, passbooks, statements of accounts, demand drafts, etc.

Anywhere banking by eliminating branch banking.

Provision of banking services 24 X 7.

Fast payment processing through Internet banking, mobile banking.

Anytime anywhere banking through ATMs.

All branches access applications from central servers/datacenter, so deposits made in any branch reflect immediately and the customer can withdraw money from any other branch throughout the world.

CBS is very helpful to people living in rural areas. The farmers can receive e-payments towards subsidy etc. in their account directly. Transfer of funds from the cities to the villages and vice versa will be done easily.

B] Benefits For Banks

Process standardization within bank & branches.

Retention of customers through better customer service.

Accuracy in transactions & minimization of errors.

Improved management of documentation & records – having centralized databases results in quick gathering of data & MIS reports.

Ease in the submission of various reports to the Government & Regulatory boards like RBI.

Convenience in opening accounts, processing cash, servicing loans, calculating interest, implementing changes in policies like changing interest rates, etc.

In India, most of the private sector banks have implemented the Core banking solutions but most of the cooperative banks, Regional Rural Banks are missing the benefits of CBS.

With the help of core banking & the latest technology, private sector banks are giving tough competition to urban co-operative banks(UCBs) & other government-managed banks.

To cope up with the growing needs of customers; co-operative banks need to implement core banking solutions. To face the challenges of a dynamic market, UCBs need to take the help of IT in their operations. Considering the importance of the matter, the Reserve Bank of India (RBI)   mandated a deadline for Urban Co-operative Banks (UCBs) and advised to implement the core banking solutions (CBS) by December 31, 2013. In India, many IT organizations like ESDS Software are helping in the implementation of cost-effective core banking solutions & Turn-Key Datacenter solutions.

ESDS

Related posts:

Core Banking and choosing the right solution

Advantages of GIS Technology in Core Banking

Core Banking has changed the way you Bank!

Discover How: IoT Drives the Future of Banking & Financial Industry

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