Ask the Data Science Guru: AI & Machine Learning in Credit Risk Management
What opportunities do AI and machine learning bring to SMB credit risk management?
Welcome to Part 2 of our 3 Part Series – “Ask the Data Science Guru”. Our last blog focused on answering the question, “What role does cash flow data play in the future of credit risk?”. Today we asked our credit science team about the opportunities presented by AI and machine learning for SMB credit risk management.
Here’s a short version of what they had to say:
Advanced computational systems that include AI and machine learning are game changers when it comes to improving the way we lend to SMBs and navigate credit risk. AI and machine learning open the door to personalization, adaptation to new information, and self-learning. These attributes can greatly advance the accuracy of small business credit risk analysis while also:
Reducing manual efforts involved in credit applications
Increasing the scale of automated underwriting
Uncovering previously undetected patterns that correlate with higher likelihoods of default – or timely opportunities for relationship growth
Credit risk is a growing AI use case because you can capture and learn from a wider net of borrower variables and economic indicators. This in turn leads to sharper credit analysis and better credit decisions.
Key areas where AI and machine learning is applied in SMB credit risk management include:
Automating existing processes around the gathering and leveraging of large volume data sets, such as banking transactions
Classifying, sequencing, and interpreting real-time data about borrower habits, business variables, and market conditions – including cash flow derived from banking statements
Feeding data to adaptive machine learning models to improve credit decisioning and automate a larger percentage of underwriting
Establishing account-level views to continuously monitor for changes in credit risk and predict potentially delinquent accounts
While AI and machine learning can boost operational efficiency and provide more accurate credit risk analysis than a static lending model, these technologies will not replace relationship-based business lending, now or in the future. There will always be a need for the personal touch of an expert and empathetic advisor to guide the borrower through the process.
Indeed, through intelligent profiling, signal detection, and annotation, AI technologies will increasingly help community-minded credit unions, banks and specialty lenders make it to that last mile – more efficiently and with greater confidence.
The perceived costs and complexity associated with AI continue to hinder small to medium sized financial institutions, highlighting the importance of choosing the right technology partner to help guide your AI and machine learning journey — one that can show proven credit science results.
If you are interested in talking to the JUDI.AI credit science team about implementing a roadmap for your AI journey, contact [email protected].