Big data is all set to revolutionise the way loans are handled. Instead of SMEs having to wade through piles of paperwork, talk to lenders and make countless trips to the lending office or bank. Data is transforming the loan industry like never before. Decisions today are not being based on ad-hoc or inaccurate inferences, the consequences of which is that data is helping lenders arrive at better decisions for SME loans. Here are five critical areas where data and data analytics is making a sizeable contribution to business funding.
Offer insights for improving accuracy in decision-making
Automated data collection, aggregation, validation and analytics ensure consistency and accuracy in gathering insights. Many SMEs might not possess adequate credit history, making them less favourable candidates for a loan. However, with data analytics many lending companies rely upon unorthodox methods to gauge the success of the business and also the likelihood of an entrepreneur returning the amount loaned for its business funding.
Customize the metrics or ratios used to determine thresholds
Data analytics plays a critical role in establishing measurable benchmarks for determining loan thresholds. Numbers can help lenders get an understanding of a sector, and its typical and expected growth trends, and then evaluate the loan applicant against these customised metrics. Without the presence of data and its analysis, it is nearly impossible to arrive at an accurate representation of the industry. With data analytics, however, the decision to approve or decline the loan is no longer a matter of guesswork.
Flag loans through review rather than using binary decision-making
With the backing and support of data, decisions move out of the yes-no binary zone towards a more thought-through and realistic approach. Loans are analysed and considered for approval based on parameters that reveal an accurate representation of the industry and the corresponding growth patterns of the loan borrower. Care must be taken to review all parameters regularly and take into consideration variables that may have changed over time. Predictive data analysis takes past trends and plots them to forecast future behaviours.
Develop templates for different loan types, exposures, etc.
Rather than go back to the drawing board every time a loan application comes in, data analytics helps lenders prepare templates based on loan types, sector, growth trends, risks and exposures, etc.
With the terms clearly articulated in the loan template, there is little room for misinterpretations. The amount, term period, repayment schedules, etc. could be dependent on the individual loan under consideration, while the broader sections can be pre-decided and standardised for specific types of loans.
Data analytics allows the lending business to clearly mark out loans that are above specified balances. These can then be referred to the Chief Credit Officers or senior lenders within the organisation or industry for approval of the loans.
Lending or business funding is serious business. And there are risks manifold for the lending organisation. With data as its foundation, the business does its best to mitigate these risks by best calculating the probability of an SME being able to return the loan amount and the interest.
As we move forward, data analytics and machine learning will join hands to enable faster and more accurate online processing and approval of loans.