An algorithm is a procedure or formula for solving a problem, based on conducting a sequence of specified actions. A computer program can be viewed as an elaborate algorithm. In mathematics and computer science, an algorithm usually means a small procedure that solves a recurrent problem. Algorithms are widely used throughout all areas of Information Technology. A search engine algorithm, for example, takes search strings of keywords and operators as input, searches its associated database for relevant web pages, and returns results. Algorithms can perform calculation, data processing and automated reasoning tasks.
Much of the software now revolutionizing the financial services industry depends on algorithms that apply artificial intelligence (AI)—and increasingly, machine learning—to automate everything from simple, routine tasks to activities requiring sophisticated judgment. These algorithms and the analyses that support them have become progressively more sophisticated as the pool of potentially meaningful variables within the Big Data universe continues to proliferate.
When properly implemented, algorithmic and AI systems increase processing speed, reduce mistakes due to human error and minimize labor costs, all while improving customer satisfaction rates. Credit scoring algorithms, for example, not only help financial institutions optimize default and prepayment rates, but also streamline the application process, allowing for leaner staffing and an enhanced customer experience. When effective, these algorithms enable lenders to tweak approval criteria quickly and continually, responding in real time to both market conditions and customer needs. Both lenders and borrowers stand to benefit.
For decades, financial services companies have used different types of algorithms to trade securities, predict financial markets, identify prospective employees and assess potential customers. Although AI driven algorithms seek to avoid the failures of rigid instructions-based models of the past—such as those linked to the 1987 "Black Monday" stock market crash or 2010's "Flash Crash"—these models continue to present potential financial, reputational and legal risks for financial services companies.
In today’s world, we see algorithms directing almost all segments of consumer technology. From analyzing SMSs, utility and credit bill payments, social media profiles, e-commerce purchase patterns, mobile phone usage and behavioral patterns to evaluating educational and professional backgrounds of individual, algorithms play the primary role. But algorithms built on an individual’s digital footprint are drastically revolutionizing the lending business.
The days when people had to wait eagerly for months to get a loan sanctioned with or without collateral are over. The absence of a sound financial track record made many banks and other financial institutions reluctant in approving their loan applications, be it for starting a new business or buying a home or buying a vehicle. When assessing potential borrowers, lenders historically focused on very limited types of data relating to their repayment capacities and credit histories. They witnessed a constant challenge in finding the right fit of consumer profiles and suffered at the hands of high turnaround time.
Such an inefficient and time-consuming market for financial products in India resulted in high rejection rates in the loan ecosystem. However, in recent years, the emergence of Big Data analytics and algorithms prompted many lending instituitions to analyse non-traditional types of data that are not directly related to creditworthiness. Such data can be collected from a variety of sources like consumers’ search histories on the internet, online shopping patterns, social media activity and various other consumer-related inputs.
Today, startups in the lending space are promising improved customer experience, streamlined processes, competitive rates and instant loan approvals. The software that is being used to revolutionize the lending industry depends on algorithms that apply artificial intelligence (AI), machine learning and other decision-making tools. When properly implemented, these algorithms increase the loan processing speed, reduce mistakes due to human error and minimize labor expenses in order to improve customer satisfaction rates. They also enable the lenders to swiftly tweak the approval criteria and respond to both the market conditions and customer needs in real time, creating wide-ranging benefits to both lenders and borrowers.
Lending to SMEs is considered yet another risky affair in India due to the dearth of credit scores and adequate data points. Traditional banking institutions use a few pre-defined data sets to estimate the financials of a business such as the balance sheets, bank statements as well as the loan repayment history of the business or its owner.
Other aspects that are gauged are credit scores assigned by Credit Rating Companies like CIBIL and collaterals provided for securing the loan. But new-age lending start-ups are emphasizing more on the unconventional records like mobile GPS data, which shows the locations visited by an applicant. This helps recognize whether the business owner regularly goes to the place of work. Use of novel methods like psychometric tests also helps in assessing the credit worthiness of applicants as it unveils the personality traits with a series of subtle questions that need not necessarily have the right answers but can reveal significant truths about the person’s entrepreneurial ability, zeal, drive and financial discipline.
This is a major tech revolution where information from several spheres can be used to study customers and quickly decide whether to grant a loan or not. Such data can not only help the fintech companies in reducing the response time but also focus on more value addition and customer-related functions. This clearly indicates that by implementing “algorithm-enabling” technology, lending firms will not only enjoy the freedom of profoundly changing the value proposition for their customers but will also manage to catapult their business well ahead of their competitors.
Nevertheless, consumer financial services companies in particular must be vigilant in their use of algorithms that incorporate AI and machine learning. As algorithms become more ingrained in these companies' operations, previously unforeseen risks are beginning to appear—in particular, the risk that a perfectly well-intentioned algorithm may inadvertently generate biased conclusions that discriminate against protected classes of people.
(Courtesy : CXO Today)