Artificial intelligence (AI) is rapidly transforming the world of credit underwriting. Credit underwriting is the process of evaluating loan applications to determine the risk involved in lending money to a particular borrower. AI and machine learning models are now being used by banks and financial institutions to automate parts of the underwriting process and improve credit risk assessment.
What is Credit Underwriting?
Credit underwriting involves analyzing a borrower’s creditworthiness based on their financial history, income sources, collateral, and other factors. Underwriters assess the applicant’s ability and willingness to repay debt. They use statistical models and human judgment to approve or deny loan applications and determine credit terms like interest rates and loan amounts.
Traditionally, underwriting has been a manual process relying on the underwriter’s expertise. But it is often slow, inconsistent, and prone to human bias. AI promises to bring greater speed, efficiency, and objectivity to credit risk modeling.
The Rise of AI in Banking and Finance
AI adoption in the financial sector has accelerated in recent years. According to a survey by the Institute of International Finance (IIF), 30% of banks reported using AI in credit underwriting processes in 2021, up from just 8% in 2019. By 2023, the portion is expected to reach 54%.
A Deloitte Insights report predicts that AI could free up to 30% of bank employees’ time spent on manual tasks like data processing and documentation. Banks expect operational cost savings of 22% by 2030 with the automation of mundane, repetitive tasks.
Leading AI applications in banking include:
- Fraud detection
- Personalized recommendations
- Process automation
- Forecasting and predictions
- Credit underwriting and risk modeling
The most significant impact is expected in credit underwriting as AI models get better at predicting risk and default probabilities.
How AI is Transforming Credit Underwriting
AI is bringing three major shifts to the underwriting process:
1. Automating manual tasks
AI can take over time-consuming data entry, verification, and documentation tasks from human underwriters. Software robots and computer vision tools can extract information from documents and applications to auto-fill forms and paperwork. This reduces repetitive manual work so underwriters can focus on complex decision making.
Startups offering an AI-based data extraction tool for underwriters. It uses optical character recognition and machine learning to pull financial information from various documents and feed it into underwriting systems. This eliminates up to 70% of manual work, per the company.
2. Using alternative data sources
AI algorithms are able to synthesize both traditional and alternative data from various sources to paint a fuller picture of borrowers’ creditworthiness. This includes:
- Bank account transaction history
- Payroll and accounting systems
- Online footprint and social media
- Mobile phone usage patterns
- E-commerce transactions
- Data from IoT devices
In the past, underwriters lacked the capabilities to efficiently analyze large volumes of unstructured data. AI models can take in thousands of data points to uncover insights that may not be apparent to humans.
Silicon Valley-based Nova Credit uses alternative data to build credit reports for immigrants and expats who lack U.S. credit histories. Factors include university transcripts, rental payments, and utility bills to score applicants who would likely be rejected otherwise.
3. Automated decision making
The most revolutionary impact of AI is automated loan approvals and credit scoring. Traditional underwriting relies on the individual judgment of the loan officer. But now algorithms can statistically determine risk and decide whether to approve applications within seconds.
This removes human biases and inconsistencies from the credit process. Algorithms strictly follow the same logic when evaluating every borrower.
Banks like JP Morgan use AI to offer pre-approvals to their mortgage applicants within minutes instead of weeks. Online lenders like Upstart go even further by approving 97% of loans algorithmically with no human underwriting required. Their model can assess risk better than traditional FICO scores.
Benefits of Using AI in Underwriting
Here are some of the major benefits driving increased AI adoption:
Faster loan processing: AI reduces the time taken to underwrite loans by automating verification and document review. This improves customer experience with quicker loan decisioning. JP Morgan claims its mortgage approvals now take just 1-3 days instead of 8-12 days.
Lower costs: AI automation cuts down on expensive manual work. Cloud-based AI tools also have lower implementation costs compared to on-premise servers. This reduces the overall cost per application for lenders.
Fewer defaults: AI models like Upstart’s have an average 10-20% lower default rate compared to traditional models. Their algorithms are better at finding viable borrowers who may have been overlooked before.
Fraud reduction: AI can recognize patterns associated with fraud and suspicious activity much better than humans. This improves risk management and compliance.
Consistent decisions: AI applies the same set of rules and logic systematically to all cases. This removes unintended biases and discrepancies between different underwriters or branches.
Regulatory compliance: AI systems produce detailed audit reports to demonstrate they are making fair lending decisions and conform to regulations. This reduces compliance risk.
Digital Lending: Growing innovation in Digital Lending platforms is possible due to advancements in AI credit underwriting which is facilitating fast and secure loan processing.
Challenges in Deploying AI in Credit Underwriting
While promising, AI adoption in banking also comes with challenges:
Data quality: Low-quality, missing, or biased data can lead to inaccurate AI predictions. Extensive data cleansing and normalization is required.
Explainability: It can be difficult to understand the reasoning behind AI model results. Underwriters may be reluctant to rely on “black box” outputs. New tools are needed to increase model transparency.
Regulatory uncertainty: Questions remain around compliance and fairness of AI underwriting models. Regulations lag behind the technology.
Legacy systems: Integrating AI tools into existing bank infrastructure poses tech challenges. Core systems need modernization.
Ethical risks: Potential for AI models to amplify societal biases and unfairly discriminate certain demographics.
Cybersecurity: Critical to safeguard proprietary AI models and customer data from security breaches or misuse.
Overcoming these limitations will require multi-disciplinary teams of data scientists, compliance experts, and user experience designers collaborating closely to deploy AI responsibly.
The Future of AI in Credit Underwriting
Industry experts forecast AI transforming credit underwriting in the next decade:
- Cutting-edge models will surpass human accuracy in predicting risk levels and loan defaults. Over 75% of credit decisions could be fully automated using AI, per a McKinsey analysis.
- Alternative data from social media and IoT devices will be commonly used alongside traditional financial data to evaluate borrowers. Consumer privacy and ethics will be top concerns here.
- Cloud platforms like Amazon Web Services, Microsoft Azure, and Google Cloud will host banks’ underwriting AI applications rather than on-premise servers. This enables scalability and faster upgrades.
- Regulators will develop frameworks on using AI ethically and transparently. Model explainability and bias testing will grow in importance.
- Community banks and credit unions will also adopt AI tools to modernize and keep up with larger competitors. The barriers to AI adoption will lower.
- Fraud detection using AI will become mainstream. Anomaly detection algorithms will pick up on suspicious patterns that humans can’t discern.
In conclusion, AI-powered automation, advanced analytics, and data synthesis will profoundly reshape credit underwriting in the next 5-10 years. However, responsible AI practices are crucial for gaining public trust and overcoming regulatory hurdles on the path to widescale adoption. The financial sector must prioritize fairness, ethics and transparency while tapping the power of AI to expand access to credit and fuel economic progress.