Revolutionize credit underwriting through Machine Learning

Credit underwriting is the process of assessing the creditworthiness of a borrower before granting them credit. In today's digital age, credit underwriting has become more dynamic, leveraging digital technologies such as machine learning (ML) to enhance the speed and accuracy of credit assessments. This note will outline the concept of dynamic and digital credit underwriting and how financial institutions are leveraging machine learning globally to revolutionize credit underwriting.

Dynamic and digital credit underwriting involve the utilization of advanced analytics, algorithms, and machine learning to assess creditworthiness. Digital credit underwriting provides an efficient and speedy assessment of credit risk using automated decision-making, data collection, and analysis.

Dynamic credit underwriting dynamically adjusts the credit score based on new data and events occurring during the borrower's relationship with the lender. For instance, an individual borrower's credit score might be impacted by several factors such as their credit history, payment behavior, and the assets they hold. In dynamic credit underwriting, these factors are periodically analyzed, and the credit score is updated based on the latest data.

Machine Learning in Credit Underwriting

Machine learning is a subsection of artificial intelligence that enables systems to learn and improve from experience by identifying patterns and automatically adjusting themselves to improve the accuracy of credit assessments. Machine learning algorithms enable financial institutions to assess the borrower's ability and willingness to repay a loan, enhancing the reliability of credit assessments.

Financial Institutions and credit unions are turning to machine learning as a way to improve accuracy, speed, and efficiency in credit underwriting. By analyzing big data sets, machine learning algorithms provide lenders with unprecedented levels of insight into a borrower's creditworthiness. Machine learning algorithms can analyze vast amounts of data, including transactional histories, social media activities, and other unstructured data sources.

For instance, machine learning algorithms can be used to analyze smartphone data to ascertain a potential borrower's creditworthiness by assessing their access to financial apps and bill payments. Analyzing data from a borrower's digital footprint provides lenders with valuable insights into the borrower's creditworthiness, consequently improving their credit scoring models.

Benefits of Machine Learning in Credit Underwriting

Machine learning improves credit underwriting by reducing the time and effort needed to complete the process, minimizing risk exposure, and increasing the accuracy of credit assessments. Below are some of the benefits of machine learning in credit underwriting:

1.     Speed: Traditional credit assessments can take several weeks to complete due to human intervention, machine learning speeds up the process by analyzing vast amounts of data and providing instant credit scores.

2.     Cost-effectiveness: Machine learning significantly reduces the cost of credit assessments by automating the process, freeing up human resources, and reducing the need for manual reviews.

3.     Standardised and Accurate: Machine learning algorithms provide standardized and accurate credit assessments by analyzing data from multiple sources, assessing patterns of behavior, and identifying outliers and trends, unlike different credit analysts point of views.

4.     Enhance Risk Assessment: Machine learning provides lenders with detailed insights into borrower behavior, reducing the risk of defaults and non-performing loans.

Global Adoption of Machine Learning in Credit Underwriting

Machine learning has become widely adopted in credit underwriting globally as more financial institutions seek ways to improve the accuracy and efficiency of their credit scoring models. Below are examples of how financial institutions are leveraging machine learning to improve credit underwriting globally.

1. JP Morgan Chase: JP Morgan Chase leverages machine learning algorithms to assess credit risk and identify fraud across its banking operations.
2. LendingClub: LendingClub uses machine learning to improve its credit underwriting process, analyzing borrower data such as their employment and loan history.
3. ZestMoney: ZestMoney is an Indian fintech company that uses machine learning to build credit scores and offer loans to consumers through its platform.
4. Kabbage: Kabbage, an online lender, uses machine learning algorithms to analyze loan applications and assess creditworthiness.

Machine learning has the potential to revolutionize credit underwriting by increasing accuracy, reducing costs, and enhancing speed. Financial institutions can leverage machine learning algorithms to extract valuable insights from big data sets, leading to better credit assessments. As machine learning continues to advance, its capabilities in credit underwriting will continue to expand, and lenders will have a more accurate prediction of risk, leading to fewer delinquencies and defaults. Machine learning has the potential to transform the credit underwriting process from a traditional and often time-consuming process to a dynamic, digitized, and data-driven one.