When more and more unfortunate incidents of human tragedies occurring due to the unethical & rogue practices in Debt Collection, it is high time Lending organisations adopt Personalised and Empathetic Debt Collection Practices
In India alone, various human causalities being reported recently due to harassment by lenders from whom they had borrowed. This is even after regulators like #RBI has put stringent conditions for debt collection Practices . Earlier during the year, Kenya also saw a rise in complaints of harassment from online lenders that led SBK, the regulator, to ban a few of them. Even in a more advanced economy like Israel, government regulators had to sound warnings to collectors as threats of suicide by borrowers became more common.
What makes these incidents concerning is that most of these unfortunate events have occurred with the customers of organisations which use Artificial intelligence as their major differentiation. And this is understandably bothering the AI evangelists. As human wellbeing – the edifice of the responsible AI framework is shaken, the believers in the power of Big data and AI hoping to bring a difference in the way credit and finance function are left with tough questions to answer. While a lot of success has been achieved in customer acquisition using advanced data science and complex statistical modelling techniques, the approach has seemed to have failed at most things beyond. This reduction of AI and Big data to nothing more than a sophisticated customer acquisition tool focussed only on the top line and the bottom line is a grave concern.
It is also to be noted that these warning signs from across the globe are coming to the fore even as the world’s central banks are loosening their purses, and some pioneering work is being done around the use of data and technology for financial inclusion in emerging economies. This may well have been a window of opportunity for the financial system to take a more inclusive approach to credit away from a historically crude and coercive one. But the truth is AI and data systems have a chequered past. While no one can really question the impact that data and AI have had on human progress in areas as diverse as education, healthcare, and transportation, there have been a few genuine concerns. Just in the last week of December, American news portals reported the story of a man who was wrongly incarcerated due to some facial recognition software errors. It’s from this vantage point that one should view the problem of data and AI in finance.
Among the issues that have emerged from the media reports about the unfortunate incidents around financial institutions is the irresponsible execution of Ai- specifically a highly questionable practice where the apps send out harassing messages about non-repayment to borrowers’ close contacts. While co-obligant and joint liability concepts are not entirely new, their arbitrary application using power of data and digital technologies is extremely unethical This poses glaring questions the ethical process of execution while leveraging the power of data and digital technologies. Pattern of these incidents But what really shows through all these shortcomings is the lack of ethical usage of AI in customer engagement and the execution process. When systems are not built to make precise moves at the right time and use feedback from that move to make the next informed move, one should expect adverse reactions.
The warning signs that have emerged should make the leadership team within all the financial institutions sit up and take serious notice. With enough success achieved on customer acquisition, it’s now time to focus energies on creating systems that can serve the financial institutions’ best interests and use AI as an effective tool for customer engagement rather than coercive intrusions. AI can indeed be moulded into a great tool for enhanced customer engagement, although the possibilities of this have not been fully explored. This is changing now with some cutting edge work done by a few companies that use sophisticated algorithms combined with finesse in digital execution to create comprehensive AI and data system that caters to the entire life cycle of debt management including debt collection. With the integration of AI with parts of the system traditionally left out, such as debt collections and customer service, customers are delighted with a high-quality experience even as the AI strengthens itself.
Similarly, digital technologies-that allow for open channels to communicate with customers – are now increasingly becoming a perfectly scalable and viable strategy adopted by many lenders. An elaborate yet transparent system that allows for free customer participation along
the lifecycle is the right way forward for financial institutions apps and could solve a major part of the problem. As important as the system’s conception is the execution of it down to the details which can be ignored only to the peril of the system.
On the policy side, countries across the globe are also working on Data Protection laws in the model of EU’s GDPR, which would give customers the legal Right to Explanation. In the future, an Algorithmic Accountability Reporting framework would also be something that lending institutions may have to work on. But even with all the regulatory and policy efforts, the responsibility of building responsible AI systems would still lie on the institutions themselves as they have to build and execute systems in line with fundamentals that policy and regulation envisions.
It’s high time that business organizations start using AI beyond its simplistic forms like customer acquisition tools, chatbots, marketing tools, etc., and start using it more responsibly for over all good of society at large. This is quite possible by responsible usage of enormous power of data and digital technologies. However, this requires serious efforts from the top leadership of business organisations which involve both cultural and process change in organizations. What is encouraging is the earnest attempts are being made by a few startups in this direction – especially in the debt management space. True Accord and CreditNirvana are a few names that stand out in this regard. Now it remains the solemn responsibility of business leaders to adopt tools and platforms which can make AI more responsible and ethical, and thus, humanity gets the best out of this amazing technology.
Designed and Developed by Lending Industry Domain Experts with over 20 years of experience, Credit Nirvana is one of the World’s first ML Analytics driven and Digital First end to end Debt Collection Platforms.