#fintech power

[nyusoft]

How AI Improves Decision-Making for Financial Deicisions?

AI has fundamentally transformed decision-making in the financial industry, equipping institutions with the ability to make faster, data-driven, and more accurate financial decisions. By leveraging vast amounts of historical and real-time data, AI algorithms enhance the decision-making process, helping financial professionals, institutions, and even everyday consumers make smarter financial choices.

One of the core ways AI improves financial decision-making is through predictive analytics. AI systems analyze historical data to forecast future trends, enabling financial institutions to make proactive decisions. For example, predictive analytics helps banks assess the creditworthiness of borrowers beyond traditional credit scores. By analyzing a wider range of data, including social behavior and spending patterns, AI can predict a borrower’s likelihood of repaying a loan more accurately, leading to fairer lending practices and minimizing the risk of default.

AI also plays a crucial role in risk management. Traditional risk assessment methods often rely on rigid criteria that may miss subtle indicators of potential risk. In contrast, AI systems use machine learning to detect complex patterns that humans might overlook. For instance, in stock trading, AI algorithms can process financial reports, economic indicators, and even sentiment analysis from news sources to determine which investments carry higher risk. By continuously learning from new data, these algorithms adapt their assessments in real-time, providing financial advisors and traders with up-to-date insights that improve decision quality.

In addition, real-time decision-making is enhanced significantly by AI-driven automation. Fintech solutions powered by AI enable institutions to automate key decision processes, such as fraud detection. By analyzing large transaction datasets, AI can detect anomalies that suggest fraudulent activity within seconds, allowing banks to respond swiftly. This capacity for instant, data-informed decision-making minimizes financial losses and ensures secure transactions, bolstering consumer trust.

Finally, personalization is another area where AI is revolutionizing financial decision-making. AI analyzes individual transaction histories, spending patterns, and financial goals to recommend tailored financial products, such as investment portfolios or credit options. This level of personalization helps consumers make informed decisions aligned with their unique financial circumstances.

In summary, AI improves decision-making for financial decisions by enabling predictive insights, efficient risk management, rapid real-time responses, and customized financial advice. As AI continues to evolve, its impact on the fintech industry and financial decision-making is likely to grow, empowering both institutions and consumers to make smarter, more informed choices.

[jcmarchi]

5 Benefits and Key Features of Hotel Chatbot - Technology Org

New Post has been published on https://thedigitalinsider.com/5-benefits-and-key-features-of-hotel-chatbot-technology-org/

5 Benefits and Key Features of Hotel Chatbot - Technology Org

A hotel chatbot is an AI-powered bot that performs human conversations between hotels and their visitors or potential guests via its website, messaging apps, and social media. It works as an automated chat or discussion interface. A chatbot can be referred to by several names, including virtual assistants, digital assistants, conversational bots, and intelligent chatbots. Companies can use these chatbots to handle orders, provide product suggestions, assist customers, schedule meetings, and perform similar duties.

Here are some of the key benefits and features of using Hotel Chatbots.

A chatbot – artistic interpretation. Image credit: Mohamed Hassan via Pxhere, CC0 Public Domain

 24×7 response time

Customer service or contact center hours are often used to determine service availability. Chatbots, on the other hand, are always available to handle leads. It allows real-time communication between website visitors and hotel organizations, increasing customer trust. Furthermore, it makes it possible for hotels to react to guest requests anytime, ensuring ongoing service even during peak seasons and holidays.

Data Collection

An AI-powered chatbot can collect and analyze huge data about customer interactions, preferences, and behavior. Hotel management can use this information to make pricing choices, conduct promotional campaigns, and offer service upgrades. Furthermore, these chatbots can be great lead-generation tools, converting new leads into customers via follow-up processes or targeted marketing campaigns. For instance, Marriott’s Enhanced Internet service runs chatbots on its website servers to gather guest data.

Upsells Growth

Chatbots can boost the upselling potential by providing a personalized customer experience. Hotels can generate personalized offers for guests by providing hotel upgrades, spa treatments, on-site restaurants, and other amenities. They can also consider cross-selling opportunities, such as tailored recommendations for special discounts.

Cost Reductions

Chatbots have the potential to save your hotel up to 30% on these expenses. Hotels may save money by automating customer care tasks. Using chatbots for first-level customer service also saves time and provides better customer service and prompt responses to hotel guests. A virtual assistant can reduce human support costs by reducing the need for a large staff.

Fast service

Chatbots allow hotels to service several clients at once and provide them with information quickly rather than making them wait. Furthermore, it is important to reply promptly when visitors make complaints or urgent requests. Therefore, chatbots and human agents should work together to resolve these issues as soon as possible. Notably, several hotels such as Amtrak also offers free WiFi, making it easy for guests to access the internet and communicate with chatbots.

Better Visiting Experience

Visitors can learn about the booking process, pricing, and availability through an AI-powered assistant. It may also provide quick answers to frequently asked questions (FAQs) and detailed information about hotels and the surrounding community. AI chatbots can assist visitors throughout their journey by communicating at every stage.

[isabellaseraphinabloger]

Unlocking the Potential: How Blockchain is Transforming the Fintech Landscape

In the realm of financial technology, blockchain stands as a beacon of innovation, promising to revolutionize the way transactions are conducted and financial data is managed. Blockchain in Fintech: The Power of Blockchain Technology in Fintech Evolution is not just a concept; it's a tangible force reshaping the industry's foundations. Let's delve into how blockchain is set to redefine the future of fintech.

Blockchain technology, at its core, is a decentralized ledger system that records transactions across a network of computers. This distributed ledger eliminates the need for intermediaries, such as banks or payment processors, streamlining processes and reducing costs.

One of the key ways blockchain is poised to transform the fintech sector is through enhanced security and transparency. The immutable nature of blockchain ensures that once a transaction is recorded, it cannot be altered or tampered with. This feature mitigates the risk of fraud and enhances trust among participants in financial transactions.

Furthermore, blockchain has the potential to expedite cross-border payments, a process traditionally plagued by inefficiencies and high fees. By leveraging blockchain technology, financial institutions can facilitate near-instantaneous transactions at a fraction of the cost compared to traditional methods. This not only benefits consumers by reducing transaction fees but also opens up new opportunities for businesses to engage in global commerce.

Smart contracts, another innovation enabled by blockchain, have the potential to automate and streamline various financial processes. These self-executing contracts automatically enforce the terms and conditions of an agreement when predefined conditions are met. This eliminates the need for intermediaries and reduces the risk of human error, ultimately saving time and reducing costs for all parties involved.

Moreover, blockchain technology holds the promise of democratizing access to financial services, particularly in underserved regions where traditional banking infrastructure is lacking. By providing individuals with access to digital wallets and decentralized financial services, blockchain can empower the unbanked and underbanked populations, fostering greater financial inclusion on a global scale.

As the fintech landscape continues to evolve, blockchain stands out as a transformative force driving innovation and reshaping traditional paradigms. From enhancing security and transparency to facilitating cross-border payments and promoting financial inclusion, the potential applications of blockchain in fintech are vast and far-reaching. Embracing this technology presents boundless opportunities for businesses, consumers, and society as a whole to thrive in an increasingly interconnected and digital world.

So, are you ready to harness the power of blockchain and join the fintech revolution? The future of finance awaits, and blockchain is leading the way.

[reallytoosublime]

Fintech, short for "financial technology," refers to the use of technology to provide innovative financial services and solutions. It encompasses a wide range of applications and services that aim to disrupt and improve traditional financial processes, making them more efficient, accessible, and user-friendly.

Fintech companies leverage various technologies, including mobile apps, data analytics, artificial intelligence, blockchain, and more, to deliver services such as online banking, digital payments, peer-to-peer lending, robo-advisors, crowdfunding, cryptocurrency exchanges, and insurance technology, among others. These technologies enable fintech companies to streamline processes, reduce costs, and enhance the overall customer experience.

Fintech has the potential to reach individuals and businesses that were previously underserved or excluded from traditional banking systems. Mobile banking and digital payment solutions have enabled people in remote or unbanked areas to access financial services without requiring a physical presence.

Fintech has had a significant impact on the financial industry, promoting financial inclusion by providing services to underserved populations, increasing competition among financial service providers, and driving innovation in areas that were previously dominated by traditional financial institutions. The rapid growth of fintech has led to collaborations between fintech startups and established financial institutions, as well as regulatory changes to accommodate new financial technologies.

Understanding Fintech: The Power of Technology in Finance

[youtubemarketing1234]

Fintech, short for "financial technology," refers to the use of technology to provide innovative financial services and solutions. It encompasses a wide range of applications and services that aim to disrupt and improve traditional financial processes, making them more efficient, accessible, and user-friendly.

Fintech companies leverage various technologies, including mobile apps, data analytics, artificial intelligence, blockchain, and more, to deliver services such as online banking, digital payments, peer-to-peer lending, robo-advisors, crowdfunding, cryptocurrency exchanges, and insurance technology, among others. These technologies enable fintech companies to streamline processes, reduce costs, and enhance the overall customer experience.

Fintech has the potential to reach individuals and businesses that were previously underserved or excluded from traditional banking systems. Mobile banking and digital payment solutions have enabled people in remote or unbanked areas to access financial services without requiring a physical presence.

Fintech has had a significant impact on the financial industry, promoting financial inclusion by providing services to underserved populations, increasing competition among financial service providers, and driving innovation in areas that were previously dominated by traditional financial institutions. The rapid growth of fintech has led to collaborations between fintech startups and established financial institutions, as well as regulatory changes to accommodate new financial technologies.

Understanding Fintech: The Power of Technology in Finance

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Check and Pay North Bihar Power Distribution Company Limited Bill Online on abhieo, get up to ₹30 back.

https://www.abhieo.in/electricity30

[nu10seo]

Explore how Nu10 leverages the transformative potential Powering Fintech with web3 technology to revolutionize the fintech industry. Discover cutting-edge solutions and opportunities at the intersection of finance and blockchain, as we pave the way for a decentralized financial future. 

[robotbulls]

Powering the Future of FinTech with Machine Learning: Uncovering Groundbreaking Applications Transforming Finance and Tech Industries

Financial Technology, or FinTech, has been revolutionizing the finance world by leveraging technology and innovative approaches to provide better financial services. Machine Learning (ML) plays a significant role in this transformation, offering groundbreaking applications that are reshaping the finance and tech industries. Here are some uncommon tips and applications of ML that might not be known to most people.</p>

1. Fraud Detection with Unsupervised ML Models</h2>

While fraud detection is a known use-case of ML, what's uncommon is using unsupervised ML models for the job. These models can identify suspicious patterns and anomalies in large datasets without being explicitly trained on labeled data. Unsupervised learning can be particularly effective in detecting new types of fraud, as it doesn't rely on pre-defined labels or patterns, and can adapt to changing fraud tactics.

2. Enhanced Sentiment Analysis for Trading Strategies

Sentiment analysis is a popular technique used in trading strategies to gauge the market sentiment based on news articles, social media, and other text sources. By incorporating advanced ML algorithms like Deep Learning and Natural Language Processing (NLP), sentiment analysis can reach new levels of accuracy and provide traders with more reliable signals. This can lead to improved decision-making and better trading performance in the financial markets.

3. ML-Driven Robo-Advisors for Personalized Investment Advice

Robo-advisors have gained popularity in recent years as a cost-effective alternative to traditional financial advisors. However, most robo-advisors use simple algorithms based on historical data and fail to provide tailored advice for individual investors. By harnessing the power of ML, robo-advisors can analyze vast amounts of data, learn from user preferences and behaviors, and provide personalized investment advice. This can lead to better investment outcomes and higher customer satisfaction.

4. ML for Algorithmic Trading with Alternative Data

<p>Algorithmic trading has been a staple in the financial markets for years, but ML can take it to new heights by incorporating alternative data sources. These can include social media, satellite imagery, or even weather data, which can provide unique insights into market trends and opportunities. ML algorithms can analyze these unconventional data sources and make more informed trading decisions, potentially leading to higher returns and reduced risk.</p>

5. Credit Scoring with ML for the Unbanked Population

Traditional credit scoring methods rely on a person's credit history, which can exclude a large portion of the global population without access to formal financial services. ML can help bridge this gap by using alternative data sources, such as mobile phone usage or social media activity, to assess a person's creditworthiness. This can enable financial institutions to provide loans and other services to the unbanked population, fostering financial inclusion and economic growth.

In conclusion, Machine Learning is driving the future of FinTech by offering groundbreaking applications that are transforming the finance and tech industries. From fraud detection to personalized investment advice, ML is reshaping the way we approach finance and technology, unlocking new potential and opportunities for businesses and consumers alike.

[algoworks]

From payments to investments, blockchain technology is transforming the financial services sector. Discover how this revolutionary technology is disrupting traditional finance and paving the way for a more decentralized and secure financial future.

https://www.algoworks.com/blog/blockchain-disruption-in-financial-services-sector/

[mostlysignssomeportents]

When you hear "fintech," think "unlicensed bank"

Tomorrow (May 2) I’ll be in Portland at the Cedar Hills Powell’s with Andy Baio for my new novel, Red Team Blues.

In theory, patents are for novel, useful inventions that aren’t obvious “to a skilled practitioner of the art.” But as computers ate our society, grifters began to receive patents for “doing something we’ve done for centuries…with a computer.” “With a computer”: those three words had the power to cloud patent examiners’ minds.

If you’d like an essay-formatted version of this post to read or share, here’s a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2023/05/01/usury/#tech-exceptionalism

Patent trolls — who secure “with a computer” patents and then extract ransoms from people doing normal things on threat of a lawsuit — are an underappreciated form of “tech exceptionalism.” Normally, “tech exceptionalism” refers to bros who wave away things like privacy invasions by arguing that “with a computer” makes it all different.

These tech exceptionalists are the legit face of tech exceptionalism, the Forbes 30 Under 30 set. They’re grifters, but they’re celebrated grifters. There’s a whole bottom-feeding sludge of tech exceptionalists that don’t get the same kind of attention, like patent trolls.

Oh, and the fintech industry.

As Riley Quinn says, “when you hear ‘fintech,’ think: ‘unlicensed bank.’” The majority of fintech “innovation” consists of adding “with a computer” to highly regulated activities and declaring them to be unregulated (and, in the case of crypto, unregulatable).

There are a lot of heavily regulated financial activities, like dealing in securities (something the crypto industry is definitely doing and claims it isn’t). Most people don’t buy or sell securities regularly — indeed, most Americans own little or no stocks.

But you know what regulated financial activity a lot of Americans participate in?

Going into debt.

As wages stagnate and the price of housing, medical care, childcare, transportation and education soar, Americans fund their consumption with debt. Trillions of dollars’ worth of debt. Many of us are privileged to borrow money by walking into a bank and asking for a loan, but millions of Americans are denied that genteel experience.

Instead, working Americans increasingly rely on payday lenders and other usurers who charge sky-high interest rates, on top of penalties and fees, trapping borrowers in an endless cycle of indebtedness. This is an historical sign of a civilization in decline: productive workers require loans to engage in useful activities. Normally, the activity pans out — the crop comes in, say — and the debt is repaid.

But eventually, you’ll get a bad beat. The crop fails, the workshop burns down, a pandemic shuts down production. Instead of paying off your debt, you have to roll it over. Now, you’re in an even worse situation, and the next time you catch a bad break, you go further into debt. Over time, all production comes under the control of creditors.

The historical answer to this is jubilee: a regular wiping-away of all debt. While this was often dressed up in moral language, there was an absolutely practical rationale for it. Without jubilee, eventually, all the farmers stop growing food so that they can grow ornamental flowers for their creditors’ tables. Then, as starvation sets in, civilization collapses:

https://pluralistic.net/2022/07/08/jubilant/#construire-des-passerelles

As the debt historian Michael Hudson says, “Debts that can’t be paid, won’t be paid.” Without jubilee, indebtedness becomes a chronic and inescapable condition. As more and more creditors attach their claims to debtors’ assets, they have to compete with one another to terrorize the debtor into paying them off, first. One creditor might threaten to garnish your paycheck. Another, to repossess your car. Another, to evict you from your home. Another, to break your arm. Debts that can’t be paid, won’t be paid — but when you have a choice between a broken arm and stealing from your kid’s college fund or the cash-register, maybe the debt can be paid…a little. Of course, digital tools offer all kinds of exciting new tools for arm-breakers — immobilizing your car, say, or deleting the apps on your phone, starting with the ones you use most often:

https://pluralistic.net/2021/04/02/innovation-unlocks-markets/#digital-arm-breakers

Under Trump, payday lenders romped through America. A lobbyist for the payday lenders became a top Trump lawyer:

https://theintercept.com/2017/11/27/white-house-memo-justifying-cfpb-takeover-was-written-by-payday-lender-attorney/

This lobbyist then oversaw Trump’s appointment of a Consumer Finance Protection Bureau boss who deregulated payday lenders, opening the door to triple digit interest rates:

https://www.latimes.com/business/lazarus/la-fi-lazarus-cfpb-payday-lenders-20180119-story.html

To justify this, the payday loan industry found corruptible academics and paid them to write papers defending payday loans as “inclusive.” These papers were secretly co-authored by payday loan industry lobbyists:

https://www.washingtonpost.com/business/2019/02/25/how-payday-lending-industry-insider-tilted-academic-research-its-favor/

Of course, Trump doesn’t read academic papers, so the payday lenders also moved their annual conference to a Trump resort, writing the President a check for $1m:

https://www.propublica.org/article/trump-inc-podcast-payday-lenders-spent-1-million-at-a-trump-resort-and-cashed-in

Biden plugged many of the cracks that Trump created in the firewalls that guard against predatory lenders. Most significantly, he moved Rohit Chopra from the FTC to the CFPB, where, as director, he has overseen a determined effort to rein in the sector. As the CFPB re-establishes regulation, the fintech industry has moved in to add “with a computer” to many regulated activities and so declare them beyond regulation.

One fintech “innovation” is the creation of a “direct to consumer Earned Wage Access” product. Earned Wage Access is just a fancy term for a program some employers offer whereby workers can get paid ahead of payday for the hours they’ve already worked. The direct-to-consumer EWA offers loans without verifying that the borrower has money coming in. Companies like Earnin claim that their faux EWA services are free, but in practice, everyone who uses the service pays for the “Lightning Speed” upsell.

Of course they do. Earnin charges sky-high interest rates and twists borrowers’ arms into leaving a “tip” for the service (yes, they expect you to tip your loan-shark!). Anyone desperate enough to pay triple-digit interest rates and tip the service for originating their loan is desperate and needs to the money now:

https://prospect.org/power/05-01-2023-fintech-ewa-payday-loan-scam/

EWA annual interest rates sit around 300%. The average EWA borrower uses the service two or three times every month. EWA CEOs and lobbyists claim that they’re banking the unbanked — but the reality is that they’re acting as sticky-fingered brokers between banks and young, poor workers, marking up traditional bank services.

This fact is rarely mentioned when EWA companies lobby state legislatures seeking to be exempted from usury rules that are supposed to curb predatory lenders. In Vermont, Earnin wants an exemption from the state’s 18% interest rate cap — remember, the true APR for EWA loans is about 300%.

In Texas, payday lenders are classed as loan brokers, not loan originators and are thus able to avoid the state’s usury caps. EWAs are lobbying the Texas legislature for further exemptions from state money-transmitter and usury limit laws, principally on the strength of the “it’s different: we do it with a computer” logic.

But as Jarod Facundo writes for The American Prospect, quoting Monica Burks from the Center for Responsible Lending, a loan is a loan even if it’s with a computer: “The industry is trying to create a new definition for what a loan is in order to exempt themselves from existing consumer protection laws… When you offer someone a portion of money on the promise that they will repay it, and often that repayment will be accompanied with fees or charges or interest, that’s what a loan is.”

Catch me on tour with Red Team Blues in Mountain View, Berkeley, Portland, Vancouver, Calgary, Toronto, DC, Gaithersburg, Oxford, Hay, Manchester, Nottingham, London, and Berlin!

[Image ID: A stately, columnated bank building, bedecked in garish payday lender signs.]

Image: Andre Carrotflower (modified) https://commons.wikimedia.org/wiki/File:30_North_%28former_Pontiac_Commercial_%26_Savings_Bank_Building%29,_Pontiac,_Michigan_-_entrance_and_Chief_Pontiac_relief_sculpture_-_20201213.jpg

CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0/deed.en

[govindhtech]

NVIDIA AI Workflows Detect False Credit Card Transactions

A Novel AI Workflow from NVIDIA Identifies False Credit Card Transactions.

The process, which is powered by the NVIDIA AI platform on AWS, may reduce risk and save money for financial services companies.

By 2026, global credit card transaction fraud is predicted to cause $43 billion in damages.

Using rapid data processing and sophisticated algorithms, a new fraud detection NVIDIA AI workflows on Amazon Web Services (AWS) will assist fight this growing pandemic by enhancing AI’s capacity to identify and stop credit card transaction fraud.

In contrast to conventional techniques, the process, which was introduced this week at the Money20/20 fintech conference, helps financial institutions spot minute trends and irregularities in transaction data by analyzing user behavior. This increases accuracy and lowers false positives.

Users may use the NVIDIA AI Enterprise software platform and NVIDIA GPU instances to expedite the transition of their fraud detection operations from conventional computation to accelerated compute.

Companies that use complete machine learning tools and methods may see an estimated 40% increase in the accuracy of fraud detection, which will help them find and stop criminals more quickly and lessen damage.

As a result, top financial institutions like Capital One and American Express have started using AI to develop exclusive solutions that improve client safety and reduce fraud.

With the help of NVIDIA AI, the new NVIDIA workflow speeds up data processing, model training, and inference while showcasing how these elements can be combined into a single, user-friendly software package.

The procedure, which is now geared for credit card transaction fraud, might be modified for use cases including money laundering, account takeover, and new account fraud.

Enhanced Processing for Fraud Identification

It is more crucial than ever for businesses in all sectors, including financial services, to use computational capacity that is economical and energy-efficient as AI models grow in complexity, size, and variety.

Conventional data science pipelines don’t have the compute acceleration needed to process the enormous amounts of data needed to combat fraud in the face of the industry’s continually increasing losses. Payment organizations may be able to save money and time on data processing by using NVIDIA RAPIDS Accelerator for Apache Spark.

Financial institutions are using NVIDIA’s AI and accelerated computing solutions to effectively handle massive datasets and provide real-time AI performance with intricate AI models.

The industry standard for detecting fraud has long been the use of gradient-boosted decision trees, a kind of machine learning technique that uses libraries like XGBoost.

Utilizing the NVIDIA RAPIDS suite of AI libraries, the new NVIDIA AI workflows for fraud detection improves XGBoost by adding graph neural network (GNN) embeddings as extra features to assist lower false positives.

In order to generate and train a model that can be coordinated with the NVIDIA Triton Inference Server and the NVIDIA Morpheus Runtime Core library for real-time inferencing, the GNN embeddings are fed into XGBoost.

All incoming data is safely inspected and categorized by the NVIDIA Morpheus framework, which also flags potentially suspicious behavior and tags it with patterns. The NVIDIA Triton Inference Server optimizes throughput, latency, and utilization while making it easier to infer all kinds of AI model deployments in production.

NVIDIA AI Enterprise provides Morpheus, RAPIDS, and Triton Inference Server.

Leading Financial Services Companies Use AI

AI is assisting in the fight against the growing trend of online or mobile fraud losses, which are being reported by several major financial institutions in North America.

American Express started using artificial intelligence (AI) to combat fraud in 2010. The company uses fraud detection algorithms to track all client transactions worldwide in real time, producing fraud determinations in a matter of milliseconds. American Express improved model accuracy by using a variety of sophisticated algorithms, one of which used the NVIDIA AI platform, therefore strengthening the organization’s capacity to combat fraud.

Large language models and generative AI are used by the European digital bank Bunq to assist in the detection of fraud and money laundering. With NVIDIA accelerated processing, its AI-powered transaction-monitoring system was able to train models at over 100 times quicker rates.

In March, BNY said that it was the first big bank to implement an NVIDIA DGX SuperPOD with DGX H100 systems. This would aid in the development of solutions that enable use cases such as fraud detection.

In order to improve their financial services apps and help protect their clients’ funds, identities, and digital accounts, systems integrators, software suppliers, and cloud service providers may now include the new NVIDIA AI workflows for fraud detection. NVIDIA Technical Blog post on enhancing fraud detection with GNNs and investigate the NVIDIA AI workflows for fraud detection.

Read more on Govindhtech.com

[jcmarchi]

Benefits of using artificial intelligence in online pokies. - Technology Org

New Post has been published on https://thedigitalinsider.com/benefits-of-using-artificial-intelligence-in-online-pokies-technology-org/

Benefits of using artificial intelligence in online pokies. - Technology Org

The integration of machine learning and advanced algorithms has brought about a new level of excitement and sophistication to the gaming industry. Not only has it made the gaming experience more enjoyable, but it has also made it more engaging by providing personalized gaming.

There are many benefits of using AI-powered features in the pokies online real money Australia, and below, we will describe some of them.

Casino – illustrative photo. Image credit: Esteban López via Unsplash, free license

Personalized Gaming Experience: Tailoring Gameplay to Player Preferences

AI-powered algorithms are designed to analyze player data and create a personalized gaming experience. AI may be regarded as a virtual concierge, sifting through players’ past preferences and carefully curating games based on their tastes.

Personalized features include various aspects, such as:

recommended games,

bonus offers, and

gameplay suggestions.

With AI acting as a virtual scout, players can be assured that they will get gaming experiences that are tailor-made just for them.

Adaptive Gameplay Mechanics: Dynamic Adjustments for Optimal Experience

AI optimizes the mechanics au online pokies based on players’ behavior and preferences. This adaptive feature allows game developers to create a more personalized gaming experience for their users. AI can adjust the game’s difficulty level based on the player’s skill level, making the game more challenging for experienced players while ensuring that new players aren’t overwhelmed.

Moreover, AI can customize the game’s bonus rounds. In addition to enhancing the gameplay mechanics, AI can also improve the game’s visual components.

Predictive Analytics: Anticipating Player Needs and Preferences

Predictive analytics is a powerful tool that enables operators to gain valuable insights into player behavior, market trends, and campaign performance. By analyzing vast amounts of data using AI algorithms, predictive analytics can provide highly accurate predictions that enable operators to make informed decisions.

For example, operators can quickly pivot their marketing campaigns if predictive analytics indicate a rising trend in adventure-themed slots. In addition, predictive analytics can help operators optimize their promotions and rewards programs.

Real-time Data Analysis: Enhancing Game Performance and Rewards

With the help of AI-powered real-time data analysis, online casinos can optimize game performance and rewards. In particular, casinos can make dynamic adjustments to odds in real-time, ensuring the game remains challenging and exciting for players. Additionally, operators can offer personalized rewards to players based on their gaming behavior and preferences.

Future Trends and Implications

Artificial intelligence is changing the world of gambling and its role in the gambling industry is expected to reach new heights in 2024. Today’s casino online pokies provide a personalized, dynamic, and immersive gaming experience.

However, while AI has transformative potential in the gambling industry, it is essential to remember the importance of responsible gambling.

[isabellaseraphinabloger]

Exploring AI's Benefits in Fintech

The integration of artificial intelligence (AI) in the financial technology (fintech) sector is bringing about significant changes. From enhancing customer service to optimizing financial operations, AI is revolutionizing the industry. Chatbots, a prominent AI application in fintech, offer personalized and efficient customer interactions. This article explores the various benefits AI brings to fintech.

Enhanced Customer Experience

AI-powered chatbots and virtual assistants are revolutionizing customer service in fintech. These tools provide 24/7 support, handle multiple queries simultaneously, and deliver instant responses, ensuring customers receive timely assistance. AI systems continually learn from interactions, improving their efficiency and effectiveness over time.

Superior Fraud Detection

Fraud detection is crucial in the financial sector, and AI excels in this area. AI systems analyze vast amounts of transaction data in real time, identifying unusual patterns and potential fraud more accurately than traditional methods. Machine learning algorithms effectively recognize subtle signs of fraudulent activity, mitigating risks and protecting customers.

Personalized Financial Services

AI enables fintech companies to offer highly personalized services. By analyzing customer data, AI provides tailored financial advice, recommends suitable investment opportunities, and creates customized financial plans. This level of personalization helps build stronger customer relationships and enhances satisfaction.

Enhanced Risk Management

AI-driven analytics significantly enhance risk management. By processing large datasets and identifying trends, AI can predict and assess risks more accurately than human analysts. This enables financial institutions to make informed decisions and manage risks more effectively.

Automation of Routine Tasks

AI automates many routine and repetitive tasks in fintech, such as data entry, account reconciliation, and compliance checks. This reduces the workload for employees and minimizes the risk of human errors. Automation leads to greater operational efficiency and allows staff to focus on strategic activities.

Advanced Investment Strategies

AI revolutionizes investment strategies by providing precise, data-driven insights. Algorithmic trading, powered by AI, analyzes market conditions and executes trades at optimal times. Additionally, AI tools assist investors in making better decisions by forecasting market trends and identifying lucrative opportunities.

In-Depth Customer Insights

AI provides fintech companies with deeper insights into customer behavior and preferences. By analyzing transaction history, spending patterns, and other relevant data, AI predicts customer needs and offers proactive solutions. This level of insight is invaluable for targeted marketing strategies and improving customer retention.

Streamlined Loan and Credit Processes

AI streamlines loan and credit approval processes by automating credit scoring and underwriting. AI algorithms quickly assess an applicant’s creditworthiness by analyzing various factors, such as income, credit history, and spending habits. This results in faster loan approvals and a more efficient lending process.

Conclusion

AI is transforming the fintech industry by improving efficiency, enhancing customer experiences, and providing valuable insights. As technology advances, the role of AI in fintech will grow, driving further innovation and growth. Embracing AI solutions is essential for financial institutions to stay competitive in this rapidly changing landscape.

[mariacallous]

Five months ago, software engineer Shikhar Sachdev adopted a peculiar hobby. While his friends met for drinks or played FIFA 23 to unwind after work, he would come home, boot up his laptop, and spend hours filling out job applications, for sport.

Sachdev is content with his job at a San Francisco fintech company, but he writes a career blog in his spare time and had noticed a recurring sentiment: Job hunting these days is the worst. Friends described returning home from an exhausting day of work they hated, applying for new positions, and quickly growing discouraged by clunky application software and a low response rate. Research suggests the frustration is widespread: 92 percent of candidates abandon online job applications before completing them, according to the recruitment platform Appcast.

“You might hate your boss. But if you think that searching for jobs is worse, you're never going to change,” Sachdev says. “I wanted to try to put some data behind the claim that job hunting sucks.”

Sachdev set himself the challenge of applying to 500 software engineering jobs to observe exactly what made the endeavor more or less frustrating. Halfway through, however, he hit a snag. “I wanted to chop my head off,” Sachdev says. He scaled back his target to a still brain-melting 250 jobs across a range of industries and company sizes, chosen largely at random—companies he’d seen on billboards, for instance, or friends’ employers.

Sachdev timed each application from start to finish and for consistency always applied directly through a company’s career page—he ended up spending about 11 hours total filling applications. Since he wasn’t looking for a new position, he always stopped short of clicking “Submit” on a completed application, except for a few choice roles that piqued his interest. (He landed three interviews, but didn’t pursue the jobs.) He aimed to make each application serviceable, but wasn't as thorough as a truly ambitious or desperate job seeker would be, so he figures the times he logged are underestimates.

Sachdev found it took an average of 2 minutes and 42 seconds to fill out a job application—but that doesn’t include time spent identifying suitable roles, and the time could vary widely from job to job. The longest took more than 10 minutes, the shortest less than 20 seconds. Much of this variation sprang from the particularities of applicant tracking software.

Applying to work at a company that used Workday, for instance, took 128 percent longer than average for similarly sized companies in the same industry. Workday spokesperson Nina Oestlien called customer service a “core value” at the company and says that application timing is determined by how customers configure their applications. (Disclosure: WIRED owner Condé Nast uses Workday. Also, we’re hiring!)

Starting Over

Sachdev’s job hunting obsession was born partly from rejection. Originally from Geneva, Switzerland, he graduated from UC Berkeley in 2019 with a degree in environmental economics and philosophy. Most of his friends lived in the Bay Area, and career opportunities in the region abounded, so he resolved to stay.

As Sachdev’s senior year wound down, he began furiously applying for local jobs. But his heart sank each time he reached the portion of an application that asked if he needed visa sponsorship. Since he lacked US citizenship, he needed an employer to sponsor him, likely with a specialty H-1B worker visa. “When I would click the H-1B box, my application would go straight into the garbage,” he says. “I was getting rejections four minutes after I applied.”

But Sachdev has the tenacity to power through the uttermost tedium for months on end. And he discovered what looked like a loophole. Foreigners who earn STEM degrees from certain US institutions can work in the country for up to three years without a visa under a federal program called Optional Practical Training. “Who stays at their first job for more than three years?” he rationalized. So when the visa sponsorship question popped up in an application for a product manager role at a major tech company he wanted to work for, he clicked “no.”

After he landed an interview, Sachdev spent 40 hours scouring job sites for tips, cramming his notebook full of hypothetical questions and their responses, compiling a presentation the company required—and totally neglecting his coursework. Half a dozen interviews later, he got the job. His heart soared, but not for long. When he explained his immigration status to the recruiter, she rescinded the offer. Sachdev started over, eventually landing a job with a startup willing to sponsor his H-1B visa, and decided to parlay his experience into a career blog offering help to other hapless job questers.

Job hunters have long complained about the process, but it developed fresh annoyances after moving online starting in the mid-’90s, says Chris Russell, managing director of the recruitment consultancy RecTech Media. Online job boards like Monster and CareerBuilder flooded companies with candidates, giving rise to applicant tracking systems built to help recruiters manage the deluge.

These systems promised to save recruiters time by automatically ranking and filtering applicants based on keywords. From the perspective of applicants required to laboriously enter their information into the software, they felt like a new barrier. “These systems were built with the companies in mind,” says Russell. “They never really considered the user experience from the job seeker’s point of view.” A cottage industry sprang up of tools and résumé whisperers promising to help job seekers get past the automated scanners.

In recent years, new features like psychological assessments and “digital interviews,” in which applicants answer prepared questions into their webcams, only placed more barriers between candidates and human decisionmakers. Meanwhile, the fundamentals of hiring remain stuck in the past, says Scott Dobroski, a career trends expert at jobs platform Indeed. It takes three and a half months for most Indeed users to find a job, he says. “All the other parts of our lives have sped up. The hiring process has not caught up.”

Time Wasters

While job hunters have much to gripe about, from “ghost jobs” to the dreaded “résumé black hole,” Sachdev decided to focus his efforts on the initial application process. He identified three main factors that affected the time it took to apply: the size of a company, the industry it was part of, and the applicant tracking software it used.

Applicant tracking software was a major source of Sachdev’s frustration. The most common systems he encountered were Workday, Taleo, Greenhouse, Lever, and Phenom, which adds AI-powered features on top of systems like Workday. More established systems such as Workday and Taleo redirected him away from the careers page and made him create a separate account for each application, adding significant time and vexation. By the end of his 250 applications, he had 83 separate accounts.

Newer offerings such as Greenhouse and Lever spared him some of these frustrations. Applications through Lever, for instance, took 42 percent less time to complete than the average for similarly sized companies in the same industry.

Sachdev also spent many excruciating minutes retyping information he’d already uploaded on his résumé because software would misread it. Workday, for instance, would routinely populate the education field with “Munich Business School” even though Sachdev’s résumé clearly says he graduated from non-soundalike UC Berkeley. “Sometimes it's not even the time,” he says. “It's the mental fatigue of having to do it every single time.”

The longest application to fill out was for the US Postal Service, clocking in at 10 minutes and 12 seconds, while the shortest was that of hedge fund Renaissance Technologies, which requested only his name and résumé and consumed a mere 17 seconds. In general, Sachdev found that government applications took the longest—a trend that Indeed’s data backs up—followed by aerospace and consulting jobs. Younger industries such as online banks, AI firms, and crypto companies were amongst the least time-consuming. Legacy banks, for instance, took about four times longer to apply to than their newer online counterparts.

Sachdev also found applications to large companies more time-consuming than for smaller firms. In general, a doubling of company size added 5 percent to the average application time.

While the process was largely an exercise in repetition, Sachdev encountered a few creative takes on a musty old format. Plaid, a fintech company that provides APIs to connect software with bank accounts, invited applicants to apply via API. (Sachdev opted for the old-fashioned route, for consistency.) The gaming company Roblox let candidates apply in-game.

While hiring software has historically been stacked in employers’ favor, more job seekers are using their own forms of automation. Bots and tools like LazyApply use text-generation technology like that behind ChatGPT to automatically mass apply to jobs, to the likely chagrin of overwhelmed recruiters. When Sachdev posted his results on discussion site Hacker News, one commenter claimed to use bots to fill out job applications and ChatGPT to write cover letters and correspond with recruiters, fully taking over only at the interview stage. “Can you blame him?” Sachdev says. “Because the companies are doing it too. Their résumé parsers, their application tracking software, and their tools are also using AI. So it's almost as if the applicant now has this weapon they can use against the companies.”

An AI arms race that floods the job market with unserious applicants and insurmountable filtering tools is in nobody’s interest, however. Indeed’s Dobroski says some platforms, including his own, have begun rolling out a new approach that aims to save time on both sides, albeit also by leaning on algorithms. Instead of sending hundreds of résumés into the void and hoping for the best—“spray and pray” he calls it—candidates can list their skills, qualifications, and preferences and let AI suggest suitable jobs to apply for. “The matching really speeds up the hiring process, and it connects the candidate with employers that they otherwise may not even have considered,” he says.

Sachdev has his own ideas for what would make job applications more productive for both seekers and recruiters. First off, he advises applicants to save time and mental anguish by prioritizing employers that use simpler software like Lever and Greenhouse. For jobs he’s really serious about, he’ll try to make a human connection with the hiring manager on LinkedIn.

There’s a saying Sachdev likes, from computer science professor Randy Pausch: The brick walls are there for a reason. Facing and surmounting hurdles can help a person discover how much they want something. But if an employer erects too many barriers, “is an applicant really going to think, ‘That brick wall is there for a reason?’ Or is the applicant going to exit out of your website and go apply somewhere else?” Sachdev says. “I think it's the latter.”

[arzelace]

5 Trends in ICT

Exploring the 5 ICT Trends Shaping the Future The Information and Communication Technology (ICT) landscape is evolving at a rapid pace, driven by advancements that are transforming how we live, work, and interact. Here are five key trends in ICT that are making a significant impact:

1. Convergence of Technologies

Technologies are merging into integrated systems, like smart devices that combine communication, media, and internet functions into one seamless tool. This trend enhances user experience and drives innovation across various sectors

Convergence technologies merge different systems, like smartphones combining communication and computing, smart homes using IoT, telemedicine linking healthcare with telecom, AR headsets overlaying digital on reality, and electric vehicles integrating AI and renewable energy.

2. Social Media

Social media platforms are central to modern communication and marketing, offering real-time interaction and advanced engagement tools. New features and analytics are making these platforms more powerful for personal and business use.

Social media examples linked to ICT trends include Facebook with cloud computing, TikTok using AI for personalized content, Instagram focusing on mobile technology, LinkedIn applying big data analytics, and YouTube leading in video streaming.

3. Mobile Technologies

Mobile technology is advancing with faster 5G networks and more sophisticated devices, transforming how we use smartphones and tablets. These improvements enable new applications and services, enhancing connectivity and user experiences.

Mobile technologies tied to ICT trends include 5G for high-speed connectivity, mobile payment apps in fintech, wearables linked to IoT, AR apps like Pokémon GO, and mobile cloud storage services like Google Drive.

4. Assistive Media

Assistive media technologies improve accessibility for people with disabilities, including tools like screen readers and voice recognition software. These innovations ensure that digital environments are navigable for everyone, promoting inclusivity.

Assistive media examples linked to ICT trends include screen readers for accessibility, AI-driven voice assistants, speech-to-text software using NLP, eye-tracking devices for HCI, and closed captioning on video platforms for digital media accessibility.

5. Cloud Computing

Cloud computing allows for scalable and flexible data storage and application hosting on remote servers. This trend supports software-as-a-service (SaaS) models and drives advancements in data analytics, cybersecurity, and collaborative tools.

Cloud computing examples related to ICT trends include AWS for IaaS, Google Drive for cloud storage, Microsoft Azure for PaaS, Salesforce for SaaS, and Dropbox for file synchronization.

Submitted by: Van Dexter G. Tirado