Artificial intelligence is currently being adopted into many industries and fintech is no exception. A staggering 90% of fintech companies worldwide currently use machine learning and artificial intelligence.
Fintech has always welcomed new technology and change, and AI seems to have been welcomed with open arms. Artificial intelligence in fintech has helped improve the efficiency of financial services, provide better customer support, and predict outcomes more accurately.
How does AI change fintech, and is it for the better? The following article discusses how AI is used in fintech websites along with examples of some top-tier companies that are already using it in their services.
Artificial Intelligence in Fintech
A few applications of artificial intelligence in Fintech websites include automated systems, natural language processing (NLP), speech recognition, and machine learning.
Machine learning generally works by analyzing a large amount of labeled training data in fintech websites, identifying correlations and patterns, and predicting future states based on those patterns.
AI chatbots integrated with fintech websites can learn to communicate in lifelike exchanges by analyzing millions of examples of text, and image recognition tools can recognize and describe objects in images by studying millions of examples. In recent years, generative artificial intelligence techniques have advanced rapidly, allowing them to produce realistic text, images, music, and other media as well.
How Is AI Used in Fintech Websites for Predictive Analysis?
Considering that fintech firms are dedicated to innovation and new ideas, it is not surprising that they would adopt AI so quickly. The use of artificial intelligence can benefit all types of businesses, and financial technology and financial services are no exception.
By applying artificial intelligence (AI) to fintech websites, businesses can provide better services to their customers, develop more efficient software, and provide data that is more readily available. Well, this is just a start let’s discuss in detail how AI is used in Fintech along with a few notable examples.
1. Managing Claims and Underwriting
An underwriter determines the policy terms and prices of an insurance policy by evaluating the risks associated with a prospective client and the type of coverage selected. During this complex process, large data sets such as customer profiles, health records, etc., are analyzed where AI comes quite in handy such as:
Intelligent underwriting
In low-risk cases, Machine Learning models trained on historical data can figure out risks and automate underwriting.
Precision pricing
Rather than focusing on broad demographics for pricing, you can offer tailored prices to increase customer satisfaction and retention. AI aids in this process in the following ways:
Modeling of granular risks. Data analysis to produce highly granular and precise risk quantification using large multidimensional datasets, including behavioral data, market indicators, etc.
A dynamic pricing system. By using artificial intelligence recommendation engines to adjust pricing elements (such as premiums, interest rates, and fees) according to risk profiles.
Predicting the impact. To optimize a pricing structure, it is necessary to forecast the impact of a pricing strategy on critical metrics such as sales, customer churn, and profit margins.
Lemonade is a digital insurance company that leverages AI to streamline underwriting and claims management. Their AI-powered bots assess claims and risks, enabling instant policy approvals and payouts in low-risk cases.
2. Prediction of Credit Risks and Loan Amounts
The most common use of notable AI in fintech websites is for credit risk assessment and loan prediction, which are both based on big data.
Using artificial intelligence models, we can gain a comprehensive view of an individual’s financial behavior by analyzing alternate data sources, such as social media, mobile footprints, and browser histories. By applying AI techniques such as neural networks, decision trees, and clustering algorithms, AI can examine hundreds of different data dimensions to determine correlations with credit risk.
A retraining of the model can also ensure that the predictive capabilities are adaptive to changes in the market, consumer behavior trends, or other dynamics that affect risk as new data accumulates.
A second fintech startup, ZestFinance, uses artificial intelligence to assess borrowers with little or no credit history, or “thin file borrowers.” Underwriting models can be tailored using this technology to identify good borrowers overlooked by national credit scores.
Image Source: zest.ai
3. Optimizing Portfolios
Predictive models can enhance investment portfolios by identifying the most suitable investments for the client based on their goals, past market trends, and risk tolerance. The advantages of portfolio optimization can be viewed from the perspective of the investor and the manager. The investment goal is to maximize the returns with the least amount of risk and achieve an optimum risk-return ratio at the efficient frontier. Clients are satisfied when they receive high returns per unit of risk.
The second goal of portfolio optimization is to diversify investments, minimize unsystematic or unpriced risks, and shield investors from poor-performing securities. The advantage of having a well-diversified portfolio is that one can always remain in a comfort zone irrespective of the volatile market. A manager’s trading activity and market data analysis allow them to identify opportunities before their competitors do. Also, it enables them to help their investors by capitalizing on such opportunities as well.
Wealthfront is a robo-advisory platform that uses advanced algorithms to create, manage, and optimize investment portfolios tailored to the client’s risk tolerance, financial goals, and market conditions. The AI-based system continually rebalances portfolios and performs tax-loss harvesting to maximize returns.
4. Detecting Fraudulent Transactions
Fraudulent activities in the financial services industry can result in billions of dollars in losses each year. Traditional methods, however, are unable to cope with fraud tactics that are rapidly evolving. Using artificial intelligence offers a more sophisticated and proactive approach to solving this problem.
Machine learning and AI models can be trained on a large dataset of past fraud cases across numerous channels, including online banking, smartphone apps, in-person visits, and more. Several intricate patterns and indicators are then identified by the models that may indicate fraud. Artificial intelligence systems are capable of blocking high-risk transactions automatically while prompting further security measures such as enhanced verification as a result of suspected fraud.
AI systems utilize recurrent neural networks (RNNs) to identify anomalies by continuously tracking transactions and user activities to detect deviations from “normal” behavior patterns. Among them are sudden changes in location, frequency of transactions, amounts, payee information, etc. With the help of advanced artificial intelligence models, such as neural networks, fuzzy logic, bayesian filters, and others, fraud can be accurately detected using IP addresses, fingerprints, and other risk indicators.
Additionally, AI is constantly learning and improving its ability to detect new threats as they emerge, adapting to evolving fraud patterns.
American Express, for instance, uses deep learning models to prevent fraud. A fraud algorithm monitors every transaction worldwide in real-time and determines whether it is fraudulent within milliseconds.
5. Identifying and Predicting Churn in the Customer Base
Forecasting tools help banks to pinpoint clients who are likely to churn or close their accounts soon. Churn prediction algorithms can be employed to perform predictive analytics in finance to leverage several aspects. Some of the significant factors that are incorporated in the study include customer attributes, customer-related transactions, price-related factors, business characteristics, and customer behavior attributes.
The proactive approach to customer retention and the individualized offer can become an effective tool for banks to minimize churn rates and keep valuable customers.
Chime, a digital bank, can detect customers who are potentially losing interest through their app usage, deposits, and spending. They use automated systems to ensure that they can send relevant messages and promotions to the customers.
6. Risk Management and Market Analysis
Market sentiment, news, events, and multiple economic factors contribute significantly to the performance of financial markets. In comparison with conventional methods, AI systems are capable of more accurately predicting future risks by analyzing vast amounts of historical and current data and applying complex models.
In the case of NLP, it is capable of analyzing mass amounts of unstructured data sources such as news reports, company filings, analyst reports, etc., to identify market signals and trends.
By using these insights, coupled with quantitative data, you can create sophisticated predictive models. Several models can be used to forecast future market behavior, including stock prices and economic trends.
A further benefit of AI-based stress testing is the ability to simulate the performance of financial institutions or assets during adverse economic conditions, for instance, a recession, a market crash, a boom, etc.
HSBC created a cutting-edge tool named ‘HSBC Risk Advisory’ for traders and risk managers. An AI-powered tool lets traders run multiple what-if scenarios to determine if the numbers are above or below their risk tolerance. Within minutes, you will receive results!
7. Segmenting and Personalizing Customers Using AI
Customers are segmented according to their preferences, behaviors, and needs using predictive analytics. Customers’ needs can be personalized, services enhanced, and loyalty increased by understanding their segments. Banks and financial organizations must design strategies that meet numerous segmentation goals while complying with compliance policies and legal requirements.
Utilizing data from multiple campaigns and touchpoints, financial institutions can create a cohesive narrative based on segmentation and personalization initiatives.
A second option is to create personalized blog content based on the product usage and interest of small segments of customers. Last but not least, it is necessary to segment customers according to their buying cycle and tailor marketing efforts to meet their needs at each stage of their journey. The approach involves data-driven marketing analytics and content creation that aim to enhance customer experiences.
A few examples of using customer data are offering personalized customer service, evaluating financial advice, and proactively detecting fraud. As well, AI can be used to automate routine tasks, which enables banks to increase efficiency, reduce costs, and provide faster customer service. Finally, artificial intelligence can be used to create a conversational banking experience. With its multilingual capabilities and simplified user interaction, it is unmatched in the industry when it comes to simplifying customer interactions.
Robinhood, a stock trading app, uses AI-powered algorithms to create personalized investment portfolios for its users. By analyzing individual risk tolerance, investment preferences, and financial goals, Robinhood provides tailored recommendations. Additionally, they use AI to segment users based on their investment behavior and target them with specific investment opportunities or educational content.
8. Algorithmic Trading
Known as algorithmic trading, it is one of the most popular applications of artificial intelligence in the financial sector for rapidly finding profitable trading opportunities.
To generate tradable market signals at lightning speeds, AI algorithms can process a vast amount of data, including real-time news, research reports, and more. In advanced artificial intelligence models, such as deep neural networks, millions of data points can be analyzed to discover patterns that are reliable predictors of future price movement.
To maximize risk management, AI systems automatically adjust parameters like order sizing, timing, etc., once an opportunity has been identified.
According to many, Renaissance Technologies is one of the world’s most successful firms in the field of algorithmic trading. The Medallion Fund, the company’s flagship fund, has achieved average returns of 66% per year since 1988.
9. Robo Advisors
A robot advisor is an investment platform that utilizes artificial intelligence algorithms to manage your investments. To meet your financial goals, they analyze data and adapt investment strategies according to what you provide.
As it automates investing, it also represents a significant cost saving over the use of a professional investment manager.
Betterment.com provides online financial advice. It uses a robo-advisor to build and manage user investment portfolios automatically without requiring human intervention.
Image Source: betterment.com/mobile-app
AI’s Future in Fintech
Fintech remains a dominant force, regardless of economic conditions, and AI will only reinforce it. Using AI in fintech has some challenges and some considerations, but overall it’s beneficial, and it’s already revolutionizing how consumers use financial services.
With artificial intelligence, financial companies can better manage and analyze data, provide better customer service, and provide better advice and results.
The evolution of AI and fintech isn’t over yet. Fintech will increasingly rely on AI in the coming years, and its possibilities are endless.
The impact of AI on businesses will be significant in the coming years. To harness AI’s potential in fintech, you require an effective solution that supports your business goals. You risk losing your competitive advantage if you don’t.
Conclusion
Investing in apt tech infrastructure is necessary for banks and major financial institutions to stay ahead of these developments in predictive analysis using AI. This may involve the adoption of cloud-based solutions, the development of NLP or chatbots within the organization, and the establishment of collaborations with fintech companies. To ensure compliance and prevent fraud, strict data governance and security measures must be in place.
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