Machine learning: Transforming the lending industry

In the past few years, machine learning has flipped the script on traditional lending. Leveraging the power of machine learning, financial institutions can now achieve faster and more accurate decisions by shifting traditional decision-making from analysis of individuals to analysis of trends and patterns. As a result, the institutions are now benefited from increased customer retention and lower operational costs.

To substantiate, the global digital-lending platform market is expected to reach $20 billion by 2026 for a compound annual growth rate of 19.6% through the seven years prior.

As a result of the benefits offered by transformed lending process, including improved decisioning, happier clients, optimized cost, etc., machine learning also confers on lenders a range of ancillary 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 on the road to answering those questions:

How does machine learning work in lending industry?

Right from speeding up the underwriting process, portfolio composition and optimization, robo-advising, model validation, and alternative credit reporting, machine learning helps the lending industry by automating the processes that require intensive manual paperwork.

It does this by analyzing raw data sets and extracting meaningful insights to offer accurate decisions. Then, this information is used by the system to solve complex and data-rich problems that are critical to the banking & finance sector.

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 credit scoring. Unlike traditional banking systems, ML involves more data and more complex calculations to give lenders a better insight into the borrower’s ability to pay.

ML-derived algorithms process several layers of data, that are limited to income data and CIBIL scores. This opens the scope of alternative data scoring for lenders. ML lets lenders analyses several factors such as bills with telecommunication firms, data from social profiles, rent payments, utilities, and health checkup records to analyze the repaying capacity of the borrowers and generate a score. When the score is under the threshold set by the lender, a loan will be approved automatically.

Improved Security

Regardless of the size of the consumer base, fraud detection in the banking industry is a critical issue for all service providers. ML holds this good; it helps evaluate enormous data sets of simultaneous transactions in real-time. In addition, the ability to learn continuously minimizes the human inputs; the system can easily learn and detect unusual activities. Moreover, machine learning techniques let lenders label historical data as fraudulent or non-fraudulent.

Streamlined Compliance

As if spending billions of dollars on regulatory compliance wasn't enough, the majority of financial firms now have to comply with even more additional rules and regulations. Regulatory challenges in FinTech and banking take a lot of time and money to address. Despite this, there is no guarantee that all new rules will be followed in a timely manner.

This calls for a technology that can read and learn from a pile of regulatory documents and can detect correlations between guidelines. One such technology is RegTech (Regulatory Technology).

These are finance-focused cloud platforms with built-in machine learning algorithms that can automatically detect and monitor regulatory changes as they occur. Banking organizations can then automatically detect irregularities by monitoring transaction data. This ensures that customer transactions meet regulatory criteria.

Enriched Customer Experience

With ease to the lenders, machine learning also offers an enriched customer experience. ML algorithms are capable of delving into petabytes of data to comprehend the specific needs of individual customers, helping lenders to curate personalized offers.

To add, to the benefits, machine learning also supports a new generation of chatbots with more intelligence, human-like vibe, and better client orientation.

Since ML supported chatbots learn from each interaction, the conversations they hold become generate a more powerful, useful, and personalized impact. This is specifically good for small and mid-sized financial operators.

Handling the Challenges of Big Data

With ever-increasing consumer base and their transaction histories, the management of huge data sets in a knotty problem for the lenders. While the transaction behaviors of the customers are highly relevant to lenders for determining the net credit-worthiness of would-be borrowers with a higher degree of accuracy, the information that they desire remain locked away in datasets too vast and bewilder to get at without help from machine learning.

Machine learning in lending industry: What's next

In today's world, Machine Learning plays a crucial role in various aspects of the lending ecosystem, from assessing risk to dealing with fraud and from customer onboarding to document authentication and much more.

As ML algorithms are automating with a myriad of tasks, they are constantly learning from the huge volumes of data to bring the world closer to an automated finance system.

For most financial firms, the need is to identify the right set of use cases with an experienced machine learning services partner, who can develop and implement the right models by focusing on specific data and domain after a thorough comprehension of the expected result that is going to be extracted from various sources, transform it, and get the desired results.

The article has been written by Vineet Tyagi, Global CTO, Biz2X