David Lin, Global Head of Data Science and Credit Risk at PayU, recently spoke to The Fintech Times about the future of the digital lending market and the core trends in this industry.
What are the core challenges the digital lending market is facing?
A huge challenge facing the digital lending market is around building meaningful relationships. In a digital world, person-to-person engagement is becoming ever more redundant in the face of online interactions. Players in the digital lending market must ensure that they provide exceptional customer service and consumer experience, both in the online and offline world. They need to create engaging, user friendly platforms that are equipped to deliver interactive responses in real-time.
Not only this, but the market also needs to ensure that offerings are in line with compliance, data privacy and data security – no small feat! The industry is also under constant pressure to improve products and further enhance conversion rates by building seamless tech stacks that are embedded with state of the art data analytics and machine learning technologies.
Put simply, the need to be in a constant state of innovation, while simultaneously supplying exceptional customer service and experience, both online and offline, is relentless for businesses.
Do you agree that technology plays an integral role in improving credit score models?
Totally agree. The ability to manage big data and use machine learning/AI to build analytical platforms will be the key to enabling digital lenders to overcome market challenges. The use of both alternative data and big data, along with machine learning and other technologies, will continue to advance credit decisioning, enhance user experience and improve operational effectiveness.
And thanks to these new technologies, consumers and users have, and will continue to, expect more from providers. Lenders need to go beyond just knowing their users, they need to truly value them. The key to making users feel valued is to make them feel that the company is familiar with and responsive to their needs. This can be done through a variety of ways, for example:
• Understanding and pre-filling data that is relevant to each individual user
• Giving users relevant pre-approved offers
• Providing reward schemes for loyal customers
• Addressing complaints and calls quickly and effectively
• If a user did not qualify for offers, the lenders need to explain rationale for declining the credit in a straightforward and transparent manner.
All this can be accomplished by connecting the digital data points that each consumer collects over time. The key to improving credit score models is using technology such as AI and machine learning and continuing to collect relevant data and compile it in a way that allows credit score models to make personal decisions for individuals.
And it’s not just about how credit scores are developed. It’s also about how lenders then connect with their customers. We see digital, mobile lending as the way of the future. As consumers come to depend on their smartphones more it continues to be a great way to connect with potential customers. Emerging markets are a great example of this – take India where there are more than 300 million smartphone users who could take advantage of digital lending via their mobiles.
Digital lending is based on data management. How it can be perfected?
Effective data management, i.e. connecting the dots and bringing both structured and unstructured data together, is critical to digital lending. It’s these data points that will provide the digital footprints for underwriting, customer service, machine learning algorithms to drive better credit and user experience.
In my experience, there are four steps that are necessary to operationalising data management and data science:
1. Develop and build a data analytics platform that allows the company to continue collecting relevant data and expanding alternative data capturing.
2. Invest invest invest! Investing in data science and analytics is critical. Also, invest in business analysts who can combine data science algorithms with business expertise.
3. Build data analytics into the product as a part of its DNA. Lenders will not only have bigger data sets to work with, but will ensure the correct mindset is there from the beginning.
4. Test and learn. AI and machine learning models will continue to refine and improve customer experience and credit underwriting if businesses embrace them.
What are the best global cases of digital lending in your mind?
A good example of digital lending in action is Square who expanded from payment to small business lending, and Nu Bank who have gone from being a pureplay credit card company to becoming a digital bank. We believe that the strategy of focusing on merchants and customers is key to the success of digital lending. By providing merchants and customers with frictionless payment options, credit offerings when required and a full suite of digital banking products, they are equipped to meet their payment, finance and banking needs.
If David’s thoughts have piqued your curiosity, check our article, Digital Lending. Ok, You’re Fast & Convenient – but Can You Be Trusted?
