The role of AI in lending
Introduction
The lending industry is rapidly adopting artificial intelligence (AI) technology to enhance various aspects of the lending process. With the AI for lending market expected to grow from $10.3 billion in 2022 to $35.8 billion by 2027, it's clear that AI will continue to play an increasingly significant role in the lending industry. From improving credit approvals and product offerings to streamlining application processes and collections, AI has a wide range of applications throughout the lending cycle.
The significance of AI throughout the lending cycle
AI has revolutionized the underwriting process in the lending industry by providing a more efficient and accurate way to assess borrowers' creditworthiness. Traditional methods of underwriting, such as credit scores and income verification, are limited in their ability to capture a borrower's full financial picture. AI algorithms can analyse vast amounts of data from various sources, including bank transactions, utility bills, social media activity, and even psychometric data, to build predictive models that can help lenders make more informed lending decisions. By incorporating alternative forms of data, AI can provide lenders with a more comprehensive understanding of a borrower's financial situation, enabling them to offer more personalized loan products and terms that align with the borrower's needs and financial capacity. Allowing lenders to reduce the risk of default and improve their overall portfolio performance.
e.g Olipay in Brazil is supporting the traditionally underserved segment of the market, where customers lack a credit history. Olipay uses alternative data sources, such as an individual's mobile phone usage patterns, social media activity, and other non-traditional data points, to build a more comprehensive picture of an individual's creditworthiness.
Moreover, AI can help lenders reduce operational costs and streamline their application processes, enabling them to process loan applications more quickly and efficiently. AI-powered chatbots and virtual assistants can provide borrowers with personalized guidance and support throughout the application process, reducing the need for human intervention. This not only makes the application process faster and more convenient for borrowers, but also frees up time and resources for lenders, allowing them to focus on higher value activities such as risk assessment and customer relationship management. In addition, AI can help lenders identify and prevent fraud, by analysing patterns of suspicious behaviour and flagging potential fraudulent activity in real time. This can help protect both lenders and borrowers from financial losses and reputational damage.
AI can also help lenders improve the collections process. Through predictive analytics, lenders can identify high-risk borrowers and prioritize their collections efforts, increasing the chances of recovering debt. Additionally, AI-powered systems can analyse a borrower's communication history and use natural language processing to tailor messages that are more likely to resonate with the borrower. By using personalized messaging, lenders can build trust with borrowers and increase the likelihood of repayment.
e.g BFree, a Nigerian based debt management platform uses natural language processing to ethically collect distressed consumer loans. Models predict customer probability to repay and best repayment solution based on the data collected.
Some Key Considerations When Deploying AI Systems
However, one key concern when deploying AI systems for lending is discrimination. AI lending bias occurs when automated lending decisions discriminate against certain groups of borrowers based on their demographic characteristics. To mitigate the issue of AI lending bias, it's important to ensure that the data used to train algorithms is diverse, representative, and free from bias. Lenders should also be transparent about how their AI algorithms work and how lending decisions are made, and regulators and policymakers should continue to monitor the use of AI in lending to ensure fair and equal access to credit for all borrowers.
For instance, in an analysis of more than two million conventional national mortgage applications, the Associated Press found that Chicago lenders were 150% more likely to reject Black applicants than similar white applicants.
Further, as with any technology, AI comes with its own set of cybersecurity risks. AI algorithms rely on vast amounts of data to make decisions, and this data is often sensitive and confidential. This makes AI systems a prime target for cybercriminals. For example, a cybercriminal could potentially hack into an AI system and alter the data used to make lending decisions, resulting in fraudulent loans or identity theft.
Conclusion
In conclusion, AI is driving progress within the lending industry, with technologies such as alternative credit scoring and personalized communication streamlining and speeding up loan cycles. However, it's crucial to carefully consider how to mitigate potential biases to ensure that innovations are truly impactful.
Sources
1.https://www.marketsandmarkets.com/Market-Reports/artificial-intelligence-market-74851580.html
2.https://www.businesswire.com/news/home/20191021005123/en/ZestFinance-Announces-Rebrand-to-Zest-AI
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