How Machine Learning
Can Redefine Lending

Machine learning has done a tremendous change in the way which financial institutions operate. This article intends to shed light on “How Machine Learning Can Re-define Lending,” analyze and understand every aspect from its basic concepts to the point how it can contribute to the Lending sector.

You May Also Like: Loan Origination – A Critical Step in Loan Process

What is Machine Learning?

Machine Learning is a branch of artificial intelligence which is based on the notion that machines could learn and adapt from algorithm & experience instead of extensive programming.

Apple’s Face ID is one of the brilliant applications of machine learning. By recognizing face patterns even in the low light and dark, the facial recognition feature is turning out to be a real game changer.

Apple has used deep learning (a subfield of machine learning) for implementing face detection in iOS 10. A gist of how it was programmed could be garnered from Apple’s machine learning journal

Advantages of Machine Learning

Machine learning algorithms have been gaining popularity owing to the wide range of advantages they offer. Here are a few most common advantages of Machine Learning Algorithm:

• Pattern Recognition:

Machine learning has a wide range of algorithms dedicated to pattern recognition which would come in useful to recognise any specific patterns from a large chunk of data. This aspect of machine learning could be used in all fields, from healthcare (Image Analysis – MRI’s, X-Ray’s, etc..) to Lending Industry (Risk Analysis)

• Insightful Decisioning:

Machine Learning could filter irrelevant data, capture relevant information and process just them to offer an in-depth insight from the available data. The insights offered by machine learning algorithms could prevent users from falling prey to wrong judgements, offer them a complete picture of the current scenario and help them arrive at better-informed, insightful decisions which have a high probability of succeeding.

• Self-Modifying:

Original machine learning systems used to have just a fixed algorithm. Nowadays, the systems are mostly self-referential in nature. These new age machines are offered the ability to modify and update their algorithm through self-learning principles which ensure that the self-rewrites don’t turn out to be a catastrophe with a user-defined function that classifies the modification, weights its fitness and buckets them as an error or reward.

You May Also Like: How Mobility is transforming the Lending Sector

Disadvantages of Machine Learning

Though Machine Learning opens up a wide range of possibilities for financial institutions, it does have a dark side to it. Here are a few dark aspects of machine learning which might end up becoming a challenge:

Errors:

Machine Learning restricts human intervention to a large extent making us dependent on them. In the case of any flaws such as errors or loss of data, identifying them and correcting them could be quite a difficult task due to the complex algorithms associated with them. As a result, its susceptibility to errors might end up creating a huge problem in the economy.

Bias:

As machine learning operations are completely digital, machine learning appears vulnerable to an array of security compromises. There are chances for machine learning algorithms to develop hidden and unintentional biases from the data used to train them. As they don’t justify a specific prediction with a rational reason, human collaboration becomes vital to ensure that the suggestions/ predictions are indeed appropriate.

Time Constraint:

Machine learning relies heavily on historical data, the longer it interacts with the data, the more accurate the predictions tend to be. So, making accurate predictions immediately might not be feasible. Thought the process is rather time-consuming, it is mandatory to feed historical data and newly acquired data to the system constantly, in order to ensure that the predictions and decisions are reliable.

Machine Learning in Lending

• Reduce Operational Costs:

While there are several components which contribute to the operational costs of a financial institution, the most prominent aspect is loan decisioning. A lot of funds go into the prospect of arriving at a lending decision irrespective of the loan amount.

Machine learning automates this process, consolidates all data, processes it by taking a variety of touch points into consideration, perform a credit check, enhancing the overall experience and quality of the process while reducing the cost involved.

• Error-Free Process:

The process of lending is a most document-intensive operation. Right from the phase of loan origination to that of underwriting, most steps involved in the processing of a loan application are highly manual in nature. These problems not only increases the loan processing time but might also make the process prone to errors leading to inefficiencies.

Machine Learning would help the lenders overcome the challenges they face due to the manual processing by injecting automation and streamlining the entire loan lifecycle

• Identify Defaulters:

As the skyrocketing rate of defaults reflects on the P & L statement of the financial institutions, the cry to deduce defaults early has gotten louder. Leveraging the power of predictive analysis, machine learning could help lenders identify people who are most likely to default in a rather accurate way.

The early deduction of defaults could significantly reduce credit risks and you can also check interesting credit repair tips at Credit Solvers which indeed decreased the volume of actual defaults lessening the stake on the equity and P&L statement of lenders.

You May Also Like: Why Should You Use A Cloud-Based Loan Management Software?

• Speed up the Process:

Lending is a rather complex business process. The complexities involved in the process tend to make it a rather long and tedious process. Machine learning removes redundancy from the workflow with its intuitive analysis mechanism and speeds up the process considerably.

The automated workflow offers lenders a competitive advantage as there is less room for human error, which helps the lenders to process even a huge workload in a short span of time.

• Simplify Lending:

Traditional Lending is a very complex process. Lenders are looking for options to simplify the conventional loan workflow to decrease the time taken and cut down the operational cost involved.

Incorporating machine learning techniques would ensure straight-through processing of applications standardize the entire process, automate the business rules with its customizable rules engine and ultimately simplify the borrowing experience for the customer.

Conclusion

Borrowers expect a straight-forward loan processing which would credit the amount to their bank account instantaneously. Though it sounds impossible, the online-based alternate lenders have identified the opportunity and are leveraging technological innovations to offer a near instant lending experience to their customers.

Machine learning despite all the odds offers a high technology quotient to Financial Institutions to make lending “instant.” Developing unique machine learning algorithms has the potential to address all complex problems faced by lenders and achieve their goal through disruptive yet sustainable innovative techniques.