This is How Machine Learning algorithms Influences Credit Scoring and Insurance industries.

AI and machine learning are already being applied in the front office of financial institutions. Large-scale client data are fed into new algorithms to assess credit quality and thus to price loan contracts. Similarly, such data can help assess risks for selling and pricing insurance policies. Finally, client interactions may increasingly be carried out by AI interfaces with so-called ‘chatbots,’ or virtual assistance programs that interact with users in natural language.

Credit scoring tools that use machine learning are designed to speed up lending decisions, while potentially limiting incremental risk.

Lenders have long relied on credit scores to make lending decisions for firms and retail clients. Data on transaction and payment history from financial institutions historically served as the foundation of most credit scoring models. These models use tools such as regression, decision trees, and statistical analysis to generate a credit score using limited amounts of structured data.

However, banks and other lenders are increasingly turning to additional, unstructured and semi-structured data sources, including social media activity, mobile phone use and text message activity, to capture a more nuanced view of creditworthiness, and improve the rating accuracy of loans. Applying machine learning algorithms to this constellation of new data has enabled assessment of qualitative factors such as consumption behavior and willingness to pay. The ability to leverage additional data on such measures allows for greater, faster, and cheaper segmentation of borrower quality and ultimately leads to a quicker credit decision. However, the use of personal data raises other policy issues, including those related to data privacy and data protections.

In addition to facilitating a potentially more precise, segmented assessment of creditworthiness, the use of machine learning algorithms in credit scoring may help enable greater access to credit. In traditional credit scoring models used in some markets, a potential borrower must have a sufficient amount of historical credit information available to be considered ‘scorable.’ In the absence of this information, a credit score cannot be generated, and a potentially creditworthy borrower is often unable to obtain credit and build a credit history.

With the use of alternative data sources and the application of machine learning algorithms to help develop an assessment of ability and willingness to repay, lenders may be able to arrive at credit decisions that previously would have been impossible. While this trend may benefit economies with shallow credit markets, it could lead to non-sustainable increases in credit outstanding in countries with deep credit markets.More generally, it has not yet been proved that machine learning-based credit scoring models outperform traditional ones for assessing creditworthiness.

Over the past several years, a host of FinTech start-up companies targeting customers not traditionally served by banks have emerged. In addition to more commonly known online lenders that lend in the United States, one firm is using an algorithmic approach to data analysis and has expanded to overseas markets, particularly China, where the majority of borrowers do not have credit scores. Another firm, based in London, is working to provide credit scores for individuals with ‘thin’ credit files, using its algorithms and alternative data sources to review loan applications rejected by lenders for potential errors. Additionally, some companies are drawing on the vast amounts of data housed at traditional banks to integrate mobile banking apps with bank data and AI to assist with financial management and make financial projections, which may be first steps to developing a credit history.

There are a number of advantages and disadvantages to using AI in credit scoring models. AI allows massive amounts of data to be analysed very quickly.