• article
24/10/2023

GenAI in Fintech: The Relationship Between AI and Human Decision-Making

Can an AI truly be your trusted companion in the fintech industry? Yes, and the relationship between AI and human decision-making in fintech has never been more promising.

Dorota Jasińska

Content Specialist

Andrzej Puczyk

Head of Delivery

Even though AI is already widely used in many areas, including financial services, its full potential is still yet to be discovered and utilized. AI algorithms, fast data processing, risk and fraud detection, and more can help when making informed and strategic decisions, especially in the sector of finances and banking.

There’s no doubt that the game changing possibilities of AI in fintech are widely used in many financial services. The efficiency of AI data analysis and computing power is often used to process huge amounts of information. AI operates on the data to make predictions and recommendations depending on user needs. The conclusions made by the algorithms are a great base for decision-making in terms of dealing with fraudulent operations, investments, granting loans, and more. This is when the executive interprets the data and recommendations based on real-life experience and intuition.

AI has been used in many companies, and Erick Brethenoux, Distinguished VP Analyst at Gartner, has discussed the possibilities of AI. Brethenoux stated that AI is a useful decision-making companion and called its impact “decision intelligence.” This name greatly summarizes AI capabilities in the field of decision-making processes.

Informed decisions with improved fraud detection and prevention

Artificial intelligence combined with machine learning is a powerful tool that facilitates decision-making on whether to proceed with a client. Thanks to advanced screening and updated information concerning fraud strategies, it can effectively detect dangerous, unusual patterns and data anomalies.

Such resources allow for a proactive approach to detecting fraud attempts. Adding artificial intelligence components to usual transaction monitoring and screening systems equips the AI with all possible means for effective Anti Money Laundering checks and “Know Your Customer” screening. Having all the current information on fraud methods, AI detects threatening and suspicious actions faster and more efficiently. This allows for quick reaction and prevention of potential fraud instead of mitigating its outcome once it happens.

Customer analysis to minimize credit risk

 

 

How does it work? AI operates on the data that include transaction history, user behavior patterns, etc., to start behavior analysis. Then, it processes the data with the use of algorithms and machine learning models to establish behavioral patterns for every user. This process is continuous, and once the technology detects unusual actions or other anomalies, the user gets flagged. Any behavior that differs from the norm and set constraints is detected at an early stage and properly addressed by the company.

Once the manager gets all the information on potential fraud, it’s easier to decide on how to proceed with the threat. The comprehensive insight into the client profile and potential fraud risks ensures a conscious decision-making process and ensures a safe and secure financial environment while protecting the company from financial crime.

This is especially useful to prevent overcompensation for insurance companies. The AI can detect if the attempt to receive compensation is justified and in accordance with the client’s insurance coverage.

One of the examples of how this is implemented is feedzai.com. The company offers a RiskOps platform using machine learning with AI to prevent financial fraud. The platform is utilized by many banking institutions to analyze customer behavior, monitor transactions, or implement watchlist screening.

Data-driven decisions on investing capital and market trends

 

 

Knowing the analysis and prediction capabilities of artificial intelligence, its potential is a great source for investment advisory. AI and fast data processing allow for a comprehensive investigation of market trends. Its insights and recommendations can replace a human advisor and provide a client with hard-data calculations and conclusions that simplify the final decision.

Strategic decisions on capital investing are a great responsibility for managers and C-level executives. That’s why they should be equipped with all possible resources to facilitate this decision-making process. AI provides all the necessary means to gain useful insights on market trends and investment strategies while informing about any liabilities. Thanks to the AI’s ability to continuously process lots of data, the algorithms can produce up-to-date reports on investing trends and risks. The reports can be used as a base for future decisions on how to allot the company capital. This also allows for making informed decisions to prevent potential losses for the business.

What’s more, companies can use AI to monitor if their business assumptions are justified by confronting them with AI predictions. Algorithms are able to quickly analyze if the set KPIs are plausible and suggest adjustments if necessary. Such insights are a significant advantage in the realization of company goals. Undertaking proper actions and choosing the right decisions in the process can facilitate the implementation of assumptions and maximize performance.

AI capabilities can also be utilized in investment portfolio automation to maximize returns for investors. AI analyzes the risks and market trends to discover possibilities of safe investments in various asset classes such as stocks, real estate, or bonds. Depending on the investor’s expectations, it is possible to make the most of current investing potential. AI works based on the client’s risk tolerance and investing goals and operates on the portfolio accordingly. Having all the information gathered by the AI, investors can make conscious decisions on where to allocate their capital.

Personalized financial analysis and recommendations

 

How is AI used in investment profile automation? Depending on the customer’s needs, AI adjusts financial strategies to reach specific goals. Platforms such as Wealthfront offer a robo-advisor that uses algorithms to provide the client with investment recommendations and an automated portfolio. The platform helps in assessing customer’s risk tolerance and financial goals. Such advisors can serve as co-pilots when making crucial investing decisions.

Betterment is another platform that helps investors in reaching their financial goals. This robo-advisor offers an in-depth analysis of customer portfolios to minimize losses by allocating funds to low-risk assets. AI-guided investing can significantly help in making decisions on investment strategies.

When it comes to investing decisions and strategies, AI advisory is a powerful tool that provides comprehensive data analysis, performs risk assessment, and offers guidance on how to allocate one’s assets while minimizing losses.

Decisions on how to proceed with customer credit based on AI data

The power of AI data processing is also embraced in decision-making on customer credit. Using AI in processing customer data and profiles minimizes errors in judgment. It implements personalized risk scoring and can predict the probability of default. Each customer is screened for creditworthiness and solvency, and the analysis is a base for further handling of potential crediting. AI can quickly produce a report on the customer that includes improved credit scoring to enable a more precise assessment of the customer’s creditworthiness. This allows for minimizing the risk that’s connected to lending.

Apart from streamlining the process of credit approval, AI can aid in predicting potential payoff problems by monitoring collections. It may happen that even though a client was screened and approved for credit, their life situation has changed. In such cases, their ability to settle their credit is different than when the loan was granted. The automation of detecting any irregularities in repayment is very helpful and allows AI to analyze possible options for the future. Through such analysis, AI can assist the lender in drawing a new payoff plan. It can take into consideration recent changes in payments and prepare a suggestion on how to continue. Such mitigation options can be evaluated by lenders and financial institutions and implemented to keep the collection of funds ongoing.

Assessment of client financial capabilities

 

 

How is AI used in assessing customer financial situations in crediting? For example, U.S. Bank takes advantage of a leading financial technology company Enova International. Their services include creating accessible credit for customers. Their online platform verifies the customer and proposes a credit solution suitable to the individual using AI and machine learning algorithms. This allows for quick decision-making regarding loans based on the accumulated data.

The AI capabilities of data processing are a powerful tool in credit approval automation. Starting from complex screening and preparing a comprehensive customer profile, it can easily prepare necessary reports. AI can also detect any irregularities in loan repayment and reassess the customer’s financial situation. That allows for drawing a new payoff plan if necessary. This way, the AI provides the management with all the necessary information on how to proceed with the customer.

Conclusion

The evolution of AI with machine learning has tremendously influenced the banking industry and financial sector. AI’s capabilities and constant learning allow for accurate and comprehensive analysis of customer profiles. AI can be used to easily monitor transactions and user behavior, flag inconsistencies, and help mitigate fraud risk. It’s a useful tool in decision-making regarding anti-fraud strategies, investments, and crediting. Considering all its capabilities, it is a great advisor in terms of making decisions on how to handle potential risks in the banking and financial sectors. Access to its reports, predictions, and recommendations is an invaluable asset when it comes to strategic decision-making processes.

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