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How Artificial Intelligence, Machine Learning Are Taking Alternative Lending Data Into the Future

alternative lending artificial intelligence machine learning

According to International Data Corporation (IDC) estimates, the digital universe is doubling in size yearly and would reach 44 ZB in 2020, up from 4.4 ZB of data generated in 2013. It is widely anticipated that big data technology and the service market will reach $41.5 billion by 2018 registering a compounded annual growth rate of 26.4%, or six times the growth rate of the overall information market. The capacity to draw useful feedback and to use that information to make operational and financial gains will be the biggest differentiator between companies. Data is the new oil of the 21st century and you need to be mining it to succeed.

Artificial Intelligence and Fintech

The fintech industry is no different; artificial intelligence (AI) has taken marketplace lending by storm. It has enabled players to cut costs, automate a variety of their processes (most importantly, their underwriting) and shorten the approval process.

AI-powered fintech startups are redefining customer service. Companies are using AI-powered chatbots as virtual assistants, customer care representatives, marketing executives, and sales reps. AI and machine learning facilitate the processing of massive troves of data and information about customers. The inference yielded can be used to develop products and services that are tailored to individual customers’ preferences, and, in the bigger scheme of things, it means greater customer satisfaction.

Below are some of the ways artificial intelligence is changing fintech:

  • Increasing Security – Security is one of the underlying reasons why many fintech companies are incorporating AI into their processes. Traditional security methods like anti-viruses are not capable enough to protect companies from malicious cyber attacks, and that’s why companies are looking for next level security. The answer is artificial intelligence. AI uses machine learning, which allows companies to fine-tune their system and be better equipped against hacker and cyber attacks. Also, this technology can aid organizations in identifying:
    • Fraudulent behavior
    • Suspicious transactions
    • Potential future attacks
  • Reducing processing time – AI helps in keeping data safe and secure, but it also substantially helps reduce data processing time. Processing loan applications in mere seconds on the back of AI-powered algorithms is the reason why fintech companies have raced ahead of brick-and-mortar banks and other financial institutions. Loan documentation used to be time-consuming and error-prone, but it is now being executed faster, cheaper, and error-free because of artificial intelligence.
  • More Automation – Startups are leveraging AI to reduce manpower and the associated bloated-cost structure. Fintech companies are able to provide products and services at better rates than legacy banks since they have less overhead costs. Robo-Analysts are eliminating the need for credit officers and are poised to take over the role of credit decisioning from humans in the long run.
  • Valuable Insights – Every company’s survival today depends on executives’ ability to differentiate between data and noise. It is crucial that actionable insight is drawn from terabytes of data being produced by consumers on a daily basis. AI’s ability to draw discernible patterns from cluttered data sets is a game-changer. It allows organizations to tweak their business strategy as well as help in complex decision-making. A lending company would be able to analyze two profiles, which look similar from all angles, but one is refused credit and the other is offered a loan. AI will have an overarching look at the qualitative and quantitative parameters that can number to thousands for one single application. This level of insight is just not possible for a human.

Machine Learning and Fintech

In simple terms, machine learning is an extension of artificial intelligence, and empowers computers or robots with the ability to learn, analyze, and predict using algorithms that iteratively learn from data. Machine learning therefore empowers the system to learn and adapt itself.

Though machine learning (ML) is not a new concept, with big data and data mining taking a central role in organizations, ML is the next big frontier. The result is that machine learning is being used in all types of industries ranging from financial services to transportation. Living example of machine learning include Google’s self- driving car, feeds on Facebook walls depending on the likes and dislikes of the account holder, and online offers made to customers by Amazon and Netflix.

As per a McKinsey Quarterly report dated June 2015, in Europe, more than a dozen banks have replaced older statistical-modeling approaches with machine learning techniques. And because of this adoption, around 10 percent of the institutions have experienced an increase in the sale of new products, 20 percent managed to save their capital expenditure, 20 percent were able to increase their cash collections, and 20 percent saw a decline in churn.

The lending industry has the potential to achieve massive operational and strategic efficiencies by implementing machine learning. Companies like Zestcash, Progreso Financiero, and Global Analytics are expediting the lending process using machine learning and big analytics. They make borrowing quick and easy and pass on savings to consumers in the form of lower rates.

  • Credit Score – Credit score plays an imperative role in deciding whether a borrower is eligible for a loan or not. FICO score is based on age-old parameters because of which many borrowers do not qualify for loans. Machine learning enables predictive modeling in credit scoring. Machine learning and AI involve evaluating data at a larger scale and aggregate the data through wider channels like Yelp scores, social media activity, and real-time shipping trends. This is able to give a much more accurate and granular picture of the creditworthiness of a borrower. ML in particular will allow the system to start generating further efficiencies as it starts making out patterns from its own lending history. So it will be impossible to game a lending system using ML as the algorithm is self-improving.
  • Lending Process – By expediting the loan application approval process, machine learning is streamlining the entire lending process. As per a 2015 Cleveland Federal Reserve survey, traditionally, a bank takes 3-6 days to respond to a loan request and even longer in deciding the creditworthiness and final acceptance of the loan. Machine learning helps in reducing the man-hours required in analyzing creditworthiness and a borrower’s background, thus making the underwriting and origination process much more potent and accurate.

A lender can create a ‘classifier’, a simple algorithm to approve a loan application. Using machine learning, the loan application classifier can learn from past applications, leverage current information in the application and predict future behavior of the loan applicant.

  • Picking out bad loans and helps in monitoring on-going loans – Companies can use machine learning and apply the market data to see which types of loans have a higher propensity to default. Based on that, the lender can take evasive actions for such eventualities. Also, machine learning has the ability to categorize non-traditional borrowers that have not been targeted by lending companies, thus representing growth potential for the companies.


It is apparent that AI and machine learning are the future of alternative lending. With growing competition in the fintech ecosystem, companies that exploit the full potential of these technologies will definitely have the upper hand in the long term.


Written by Heena Dhir.


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