48% of companies globally use machine learning, while on the other hand 84% of Indian business decision makers are planning to adopt the technology. Why? Because it helps businesses make smarter decisions, faster. But how useful is this technology when it comes to transforming the traditional loan approval process?     

Manual loan processing involves a lot of tedious time-consuming tasks such as document collections, data verification, data entry, underwriting, etc. that end up costing the businesses in terms of manpower consumption; thereby delaying their more important growth opportunities. Let’s understand how ML models can potentially transform the traditional loan approval process and what benefits it offers to both lenders and borrowers.  

Automatic Data Extraction & Review 

Lending companies use ML algorithms and a couple of other technologies to digitize and review necessary loan application documents such as tax documents, financial statements, and more at lightning speed. For instance, Protium utilizes ML paired with advanced tools to convert scanned documents or images into texts and review the data. It helps Protium identify and extract key income figures, loan amounts, asset details, and liabilities within a few minutes.  

Faster Creditworthiness Assessment for Quick Decision-Making 

The next step is to then use a mix of ML algorithms, ML models and alternate data sources to assess the creditworthiness of business loan applications in a fast and accurate manner in the following ways: 

  1. ML Algorithms – ML algorithms can analyze data to recognize patterns in inconsistencies or anomalies in financial statements such as sudden spikes in expenses or unexplained discrepancies between income and tax returns.  
  1. ML Models – Various ML models, such as Regression, Classification and Deep learning, are used for data analysis to make informed decisions. Each model has unique analysis capabilities as follows: 
  1. Regression Models: These models are good at predicting continuous values based on historical data such as the likelihood of a successful loan repayment.  
  1. Classification models: These models excel at categorizing data points. For instance, they can analyze data to categorize applicants under high-risk, medium-risk or low-risk based on various factors. 
  1. Deep learning models: They can handle intricate data patterns to excel at tasks like identifying fraud or predicting loan defaults with high accuracy.  
  1. Use of Alternative data sources – Furthermore,  the use of alternative data sources such as business registrations on government portals, court filings, and online financial history help in gauging applicants’ creditworthiness in just minutes.  

Benefits of Machine Learning for lenders and borrowers  

 Lenders and borrowers benefit in the following manner with the use of Machine Learning at each stage: 

  1. Increased efficiency and speed:  

A study by GreenState Credit Union found that ML algorithms can be up to 70% more accurate than manual methods at identifying hidden consistencies buried within papers, translating to doubling the speed of loan approval process. 

  1. Reduced Costs & enhanced efficiency: 

Using ML, lending companies can reduce manpower cost by automating repetitive tasks, and free up employees time for more strategic work such as building relationships with borrowers and developing new loan products.  

  1. Better Risk Management  

Apart from analyzing credit risk, ML can evaluate a wider range of financial risks including market fluctuations, insurance exposures, demographic shifts, and operational efficiencies, empowering lenders to make more informed decisions and mitigate potential losses.   

  1. Customer Segmentation 

ML algorithms can help lenders segment their customer base by analyzing their preferences and behaviors to develop tailored marketing campaigns, yielding better campaign effectiveness and customer acquisition rates. 

  1. Aid in Regulatory Compliance  

Supervised learning algorithms can identify potential violations to make sure lending companies adhere to required regulatory compliance, avoiding costly fines and reputational damage.  

Machine Learning: Powering Innovation and Efficiency in Financial Services 

According to a Gartner analysis, machine learning is the top technology leveraged by financial servicing companies to bring innovations into services, improve operational efficiency, and optimize cost for growth. Moreover, the joint survey by the Bank of England (BoE) and Financial Conduct Authority (FCA) further states that the use of ML is going to increase over the coming years. These studies and surveys indicate the potential of ML to transform the traditional finance health seems promising and is likely to see new updates in future.With its proven ability to streamline processes and enhance accuracy, machine learning is rapidly transforming traditional lending methodologies, offering faster loan approvals for borrowers and improved efficiency and cost savings for lenders – a win-win for all.