AI, to put it simply, simulates the way human brains function by using computer algorithms. Simulations have shown striking similarities between both. AI even offers potential capabilities that go beyond that of the human brain, which is often limited by natural forces. The former, for instance, can continuously “think” 24 hours a day for years on end.

Apple’s personal assistant feature, Siri, for example, uses AI to help extract information or send messages for its users. Another example is automaker Toyota, which has invested billions of dollars into building accident-avoidance vehicles by utilizing AI.

In fact, AI has been developed to support many, if not all, sectors.

In the financial technology (fintech) sector, AI’s is helping to open “Pandora’s box” by unlocking the mysteries of the financial services sector, particularly in lending activities. AI can assist verification processes and manage risks more quickly and efficiently than its human counterparts. Beyond simply looking at borrowers’ capacity to repay loans, it can even help analyze their characters and behavior to evaluate their likely performance.

The availability of an invidual’s data, extracted from their social media activities and financial transaction history, enables AI to process big data, conduct analysis and produce conclusions on a person’s behavioral tendencies. The more data available, the more accurate the analysis.

In 2012, Stanford University researcher Michael Kosinski claimed he could predict the skin color (at 95 percent accuracy), sexual orientation (88 percent) and political affiliation (85 percent) of a Facebook account owner based on an average of 68 post likes.

From similar data, one can also predict a user’s intelligence, religious affiliation, rate of alcohol, cigarettes or drugs consumption and even deduce whether their parents are still together or not. This was also the reason why Facebook changed the “like” settings from public to private and took the researcher to court.

When combined with one’s transaction history, more variables can be used to provide a more detailed illustration of a person, which is useful for improving the accuracy of an analysis.

A person’s record of paying their electricity bills on time can illustrate their discipline. Regular fund transfers to an orphanage, for example, can also be considered a reference point on the character of a bank account holder.

However, the implementation of AI to such data analysis must be viewed within the context of big data analysis.

While common analysis uses a certain set of rules and parameters to get results, AI employs a trial and error mechanism. This is similar to our immediate response to touching hot objects, which causes the brain to both automatically learn and command our body to avoid such actions in the future.

This analogy applies to the use of AI in the fintech sector. From millions of data inputs from a person’s transaction and repayment history, combined with data from social media, banks can actually construct a “big brain” to analyze whether a customer deserves to obtain a loan. By entering a set of data, this “brain” can provide an “opinion,” like an expert advisor would.

The more the brain “learns” from new data, the “smarter” it becomes in generating decisions or financial recommendations. Like a chess player who practices every day, every loss or mistake serves as feedback that can be used to defeat their opponent next time.

Unfortunately, the Indonesian market is not yet ready to tap into the great potential of AI. A concerted effort is necessary to prepare a more conducive ecosystem.

The government, among others, must ensure that regulations keep pace with the latest technological advancements without creating unnecessary burdens.

On the other hand, individuals need to be more careful in managing their personal data, including those accumulated on social media. The financial industry must also process data with a sense of responsibility so that it benefits society at large.

If banks, financial service providers and fintech’s peer-to-peer (P2P) lending platforms can provide loans at an accelerated rate, they could fast-track economic growth by involving those positioned in the low-income bracket.

If lenders could confidently offer loans under Rp 100 million (US$7,504) to the lower-middle class segment, then economic distribution would improve exponentially. This would provide an answer to the financing gap challenges currently filled by informal lenders, such as tengkulak (brokers).

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The writer is a member of the Indonesia FinTech Association and CEO of PT JAS Kapital

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