Although Indonesia’s technology ecosystem may be best known for its four unicorn startups, the country is also home to some of the most innovative fintech companies in digital finance, such as OVO, Julo, Modalku and Kredivo. These ventures are stewards of financial inclusion, extending short-term capital to Indonesian entrepreneurs and consumers who did not have access to it before, and who may otherwise never have had the opportunity.
To continue serving this noble mission, Indonesian fintechs must also think more deeply about the other end of the lending process: how to recoup their disbursements from consumers in the most ethical, cost-effective and professional way. This question is as much about sustainability as it is about business. Every borrower who does not repay a loan on time represents a near-perfect one-to-one opportunity cost. The lender could have used the same money to lend capital to another unbanked consumer who would repay it responsibly. Nonperforming loans are thus a threat to both the fintech’s bottom line and social mission.
Unfortunately, there is a long-standing stigma surrounding debt collection in Indonesia. Any fintech companies in Indonesia that have sought a collection solution attest to it – there is no innovation on this side of the lending process.
The status quo in the industry involves small, third-party agencies in various cities traditionally relying on hostile phone calls and confrontational field visits to intimidate customers into paying. While some financial institutions are forced by circumstances to outsource their collections to these third-party debt collection agencies, asking someone to repay debt is never an easy endeavor, and thus they are frequently faced with suboptimal recovery rates and, even more gravely, the permanent risk of tarnishing their reputation due to reckless practices.
Financial institutions and digital lenders alike must believe in changing the paradigm. If fintechs aim to deliver end-to-end innovation across the user journey, starting with credit scoring all the way to loan disbursement, there is no reason this approach should stop at the collections stage. Poor collections of nonperforming loans will jeopardize their entire business, crippling growth and hampering their ability to serve other customers.
It is a well-known fact that new technologies such as machine learning and artificial intelligence can help digital lenders automate the processing of information and compare it to the criteria that determine credit risks. Such automation identifies would-be borrowers who are not creditworthy or less than truthful about their financial circumstances, as computers shoulder the donkeywork of routine credit checks, transaction handling and frequently asked questions. While artificial intelligence is not the end all be all in solving fintech’s problems, it does greatly improve the efficiency with which they can understand problems and deploy solutions.Unbeknownst to many fintechs, the insights derived from machine learning and artificial intelligence can now extend past underwriting and credit-scoring and even be used to shape and guide approaches to dealing with debtors. For example, artificial intelligence can identify and recommend the time of day, the tone of voice, and even the specific payment channel a specific customer would most likely respond well to, even integrating state-of-the-art technologies such as psychometric scripting. Machine learning now allows for advanced collection platforms to take over the whole process and not only determine the most suitable collection strategy for a specific segment of the population, but to also automatically deploy and manage it, while self-adjusting itself to ever-increasing efficiency gains based on previous historical results.
In this way, technology gets lenders past the idea of a singular “best practice” and opens a landscape of best practices and ethical debt recovery from a wide spectrum of different consumers. This level of personalization not only ensures that the collection agents’ efforts are optimized, it also enables fintechs to safeguard their relationships with their debtors.
There is also the question of operational efficiency to consider. As increasingly more of a fintech’s systems become integrated with machine learning and artificial intelligence, its operations can become more streamlined. And simplicity is the ultimate driver of adoption. The easier and more responsive a system is to use, the more people will inevitably use it. With complexity being a serious cause of loan delinquency, such simplicity would quite literally pay off for digital lenders.