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Benefits of AI in Credit Risk Management

The aftermath of the pandemic impacted businesses, leading to increased bridging loans and delayed investments. As the economy recovered, the need for finance has risen, and with it, came the need for effective credit risk management.



With the rise of artificial intelligence in all major aspects of life, several fintech solutions came to the forefront to solve the intricacies of credit risk management. According to Allied Market Research, AI in banking reached a value of $3.88 billion and is expected to rise to $64 billion by the year 2030.


Significance of Credit Risk Management:


Lending institutions have borne the brunt of the lack of data in credit risk management for a long.


Adopting advanced portfolio monitoring methodologies and improving data collection is imperative for banks and financial institutions to improve their credit risk management methods.


The importance of credit risk management cannot be neglected; it does not only prevent valuable cash loss, but it also assists banks in finding the right customer and the right opportunity to make it worthwhile for both parties.


Additionally, it is also important for the business that credit approvals are timely granted, and that lenders are tracked with minimum effort to make the entire credit risk management process resourceful, and to prevent cash loss amongst the lending institutions.


Benefits of AI in Credit Risk Management:


It is imperative for lending institutions to adapt to the latest advancements in machine learning and artificial intelligence to fulfil their credit risk management needs.


To utilize real-time user data, monitor customer profiles, and track borrower timelines, credit assessment tools backed by artificial intelligence can significantly reduce credit processing time.

  • AI-backed credit risk management solutions are able to work with complex data models and improve their performance over time, making them more accurate with each iteration. They also do not require any supervision and can present actionable insights with minimum human effort.

  • AI models in credit risk management can personalize and automate processes for a borrower’s entire credit cycle, utilizing their data to provide customers with a seamless experience.

  • A smooth customer lifecycle also helps improve credit decisions, shortening credit approval time significantly. AI-backed credit decisions are further helpful in riskier profiles, helping identify frauds and predicting credit risk accurately.

  • AI also helps decide maximum borrowing thresholds and adjust loan prices in real-time, according to shifting market data.



AI Applications in Credit Risk Management:


Applying AI and machine learning algorithms to the various functions of credit risk management can save loaning institutions a significant amount of funds. Here are some ways through which AI can integrate into banking to improve credit and increase revenue:


  • Credit forecasting: AI algorithms are employed by banks and other finance institutions to create models that can successfully predict the probability of default and loss severity. AI models work with complex data sets for effective portfolio management, predict risk and improve credit approval rates in the banking sector.

  • Customer Profiling: AI models utilize data points throughout the customer lifecycle, which can then successfully predict if the customer will be able to successfully pay back the loan. If an AI credit risk management solution marks a customer as likely to default, then the bank takes a few measures to bring them back on track or adjust the payback amount.

  • Fraud Detection: AI mainly helps prevent financial losses in lending institutions by ensuring compliance and detecting early signs of fraud. AI algorithms look out for patterns signifying money laundering and fraud and can help stop financial crime in its tracks, all based on real-time data.

  • Automating the Credit Lifecycle: For effective and timely credit risk management, AI models help banks in automating the various processes of the entire credit lifecycle, from customer acquisition, knowing and verifying them, profiling them for fraud detection, granting credit approval to collecting funds.


Challenges to AI in Credit Risk Management:


Transparency:

Most AI models used in the fintech industry are said to be based on the ‘black box design’, implying that the users are not aware of the inside operations that yield its results, making the issue of transparency a real challenge, especially in the field of credit risk management.


The lack of transparency also gives rise to the question of biased decision-making and lack of business support. A strong internal business communication system can effectively solve the data governance problem.


Data Quality:

It is often said that the effectiveness of an AI model is as good as its data. Data quality is of utmost importance when it comes to artificial intelligence and machine learning. Faulty, unreliable and unclean data leads to bad decision-making. Gathering data from reliable external sources, and keeping it updated and organized is crucial to getting actionable insights and results from AI models.


Compliance:

Since it is difficult to pinpoint the exact data that the AI algorithm has utilized to present its results, financial institutions find it increasingly challenging to prove its findings, leading to issues with fairness and adhering to company policy.

There are compliance guidelines in place to avoid this issue so that financial institutions can use AI fairly.


The applications of AI in credit risk management are vast and increasingly beneficial, but with any emerging technology, it comes with its share of challenges that can be overcome by using data best practices, keeping processes transparent, and following regulations.


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