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How Artificial Intelligence is influencing the Lending Industry

Lending is a big business that directly and indirectly touches almost all parts of the economy. With a substantial customer base holding trillions in loans, any technology that boosts return on investment or expands market share represents a potential goldmine.

Both established banks and startups are continuously seeking to innovate, and artificial intelligence is one such lever that can be leveraged effectively. Research shows that approximately 15% of the venture funding raised for AI vendors in the banking industry is for lending solutions 

AI is transforming customer experiences through contactless interactions, such as instantaneous credit approvals and enhanced protection against fraud and cybersecurity threats.

Role of AI in Lending

AI-based lending platforms leverage machine learning algorithms to streamline the lending process. AI-based lending involves using data analytics to evaluate creditworthiness, risk, and other factors that determine the probability of a borrower defaulting on their loan. By analyzing volumes of data in real-time lenders can make more informed decisions regarding approval threshold, loan amount, and the interest rate at which they lend.

AI, along with machine learning (ML) and Gen-AI, helps financial institutions identify borrowing patterns to reduce the risk of default. By utilizing machine learning algorithms banks can efficiently analyse large amounts of data to evaluate creditworthiness and make real-time lending decisions. One advantage of incorporating AI into lending practices is its ability to minimize bias and enhance accuracy in credit scoring.

Leveraging AI in Lending

By employing AI algorithms lenders can expedite the processing of loan applications with precision, thereby increasing loan throughput and reducing risk. Borrowers receive decisions on their applications almost real-time increasing customer satisfaction. Furthermore, this enhanced efficiency empowers lenders to handle quantities of loan applications potentially leading to augmented revenue generation for their business.

Through the application of machine learning algorithms, lenders can examine large quantities of data, identifying patterns and making more precise forecasts regarding creditworthiness. This allows lenders to minimize the risk of default and extend loans to individuals who might have otherwise been disregarded by lending models. Moreover, AI-driven fraud detection systems can effectively reject loan applications safeguarding lenders, against financial losses.

AI plays a vital role in shaping how financial institutions handle risk management encompassing security, regulatory compliance, fraud prevention, anti-money laundering (AML), and know-your-customer (KYC) protocols. By incorporating AI into their systems, banks, investment firms, and insurance companies can leverage its capabilities to conduct real-time calculations for performance forecasting. Identifying spending patterns ensures compliance adherence and explores numerous other applications.

How Banks are taking advantage of Digital Lending

Many traditional banks are now embracing intelligence (AI) to streamline their lending processes and enhance the customer experience. According to a report, 80 % of banks recognize the potential benefits that AI can bring to the banking industry. By utilizing machine learning algorithms banks can efficiently analyze amounts of data to evaluate creditworthiness and make real-time lending decisions.

One advantage of incorporating AI into lending practices is its ability to minimize bias and enhance accuracy in credit scoring. Algorithms can consider factors beyond just credit history, such as social media engagement and transaction data, which provides a more holistic assessment of an applicant’s financial well-being. However, there are concerns regarding bias and the importance of transparent decision-making.

Numerous banks have already implemented AI-powered lending platforms, like JP Morgan Contract Intelligence (COiN) platform and Wells Fargo’s Digital Lending Platform. These platforms utilize natural language processing and predictive analytics to automate loan document review processes while improving risk assessment.

Emergence of Digital Lending Platforms

The lending industry has been greatly influenced by Artificial Intelligence (AI) which has reimagined how financial institutions function, leading to efficiency, accuracy, and customer focus. Various forms of AI-based lending platforms have emerged to cater to different stages of the lending process.

Peer-to-peer lending (P2P) is a form of lending that directly connects borrowers with investors through a platform. Banks and financial institutions are removed from the workflow thereby offering borrowers lower interest rates and fees. For investors seeking to diversify their portfolio and achieve returns other than those offered by traditional savings accounts or bonds, P2P lending presents an enticing opportunity. Notable platforms in the P2P lending space include LendingClub, Prosper, and Funding Circle.

Crowdfunding and AI have become two agents of change in the lending industry. There are over 1200 crowdfunding platforms worldwide. The combination of these two forces has given rise to lending platforms that offer unique advantages and risks for both borrowers and lenders.

Research predicts that the global market for AI platform lending will reach a revenue of USD 90 billion by 2033 with a projected compound annual growth rate (CAGR) of 25%.

With the digitization of the lending industry, AI has become a major factor in evaluating creditworthiness and mitigating risks. Credit scoring models powered by AI can swiftly and accurately assess a borrower’s creditworthiness enabling lenders to make well-informed choices. However, there are disadvantages associated with exclusively relying on AI for credit scoring and risk assessment. Sometimes the data used to train these AI models is not a reflection of the entire population and it can lead to biased outcomes. The other issue with AI models is ‘Traceability’ which is mandatory for regulatory compliance. SR 11-7 is a regulatory standard set out by the U.S. Federal Reserve Bank that gives guidance on model risk management which needs to be implemented.

One of the hurdles to adopting AI in the lending industry revolves around privacy concerns. As AI algorithms advance, they can gather and analyze borrower data, including sensitive details like income, employment history, and credit scores. This raises questions about how this information is utilized who can access it and how it is safeguarded against unauthorized access or hacking attempts.

While the usage of AI will increase exponentially in the ‘Lending’ space, one big caveat is that any system is only as good as the people who set it up. Many of these applications are new and have only really existed during a time of modest economic growth. It is easy to appear right about the worthiness of loans during good times; the real test is often how they do in a downturn.

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