In the past few years, machine learning has changed the script for traditional lending. By harnessing the power of machine learning, financial institutions can now make faster, more accurate decisions by shifting traditional decision-making from individual analysis to trend and pattern analysis. As a result, institutions now benefit from higher customer loyalty and lower operating costs. For confirmation, the global digital lending platform market is expected to reach $ 20 billion in 2026, an average annual growth rate of 19.6% over the past seven years.
Because of the benefits that the transformed lending process offers, including better decision-making, happier customers, optimized costs, etc., machine learning also gives lenders a variety of additional leverage points, including:
- Improved processing efficiency
- Improved accuracy
- Streamlined compliance
- Efficient analysis of data in large volumes
But how does machine learning work and how do lenders actually use it? Let’s start by answering these questions:
How does machine learning work in lending industry?
From accelerating the underwriting process, portfolio composition and optimization, automated advice, model validation and alternative credit reporting, machine learning is helping the credit industry by automating processes that require intensive manual paperwork. It does this by analyzing raw data sets and extracting meaningful Information to make accurate decisions. This information is then used by the system to solve complex data problems that are critical to the banking and finance sectors.
A notable use of machine learning in lending industry
Faster Loan Approvals with Minimal Risk
One of the most useful applications of machine learning is in credit scoring. Unlike traditional banking systems, ML involves more data and more complex calculations to give lenders a better idea of the borrower’s ability to pay. Algorithms derived from ML process multiple layers of data limited to sales data and CIBIL scores. This opens up the scope for alternative data scoring for lenders. ML enables lenders to analyze various factors such as telecommunications company bills, social profile data, rent payments, utilities, and medical exam records to analyze borrowers’ solvency and create an assessment. If the score is below the threshold set by the lender, the loan will be automatically approved.
Improved Security
Regardless of the size of the consumer base, fraud detection in the banking sector is a critical issue for all service providers. ML considers this; helps to evaluate large amounts of simultaneous transaction data in real time. In addition, the ability to continuously learn minimizes human input; the system can easily learn and recognize unusual activity. In addition, machine learning techniques enable lenders to mark historical data as fraudulent or non-fraudulent.
Streamlined Compliance
As if spending billions of dollars on regulatory compliance wasn’t enough, most financial firms now have to follow additional rules and regulations. There is no guarantee that all new rules will be followed in a timely manner. This requires technology that can read and learn from a stack of regulatory documents and identify correlations between policies. One such technology is RegTech (Regulatory Technology). These are finance-oriented cloud platforms with built-in machine learning algorithms that can automatically detect and monitor regulatory changes as they occur. Bank organizations can automatically detect anomalies by monitoring transaction data. This ensures that customer transactions meet regulatory criteria.
Enriched Customer Experience
For lenders, machine learning is also a rich customer experience. ML algorithms are able to search petabytes of data to understand the specific needs of individual customers and help lenders choose personalized offers. It also supports a new generation of chatbots with more intelligence, a more human environment, and better customer focus. As ML-enabled chatbots learn from every interaction, the conversations they have have a stronger, more useful, and more personalized impact. This is especially good for the little ones and medium-sized financial service providers.
Handling the Challenges of Big Data
With a growing consumer base and their transaction history, managing huge records is a difficult problem for lenders. While customer transactional behavior is of great importance to lenders in determining the net creditworthiness of potential borrowers with a higher degree of accuracy, the information you want remains stored in records that are too large and confusing to be accessed without them.
Machine learning in lending industry: What’s next
In today’s world, machine learning plays a vital role in various aspects of the credit ecosystem, from risk assessment to fraud handling and from customer onboarding to document authentication and much more. Learn from the vast amounts of data to bring the world closer to an automated financial system. For most financial firms, there is a need to identify the right use cases with an experienced machine learning services partner who can develop and implement the right models. Focus on specific data and domains after fully understanding the expected result drawn from various sources, transform it and get the results you want.
