There is a growing appetite amongst lenders to use alternative data for reasons beyond credit lending or risk management. From personalising the customer experience and managing an account better to improving collection processes, there is a marked interest from major industries to use both trended and alternative data across the entire customer lifecycle.
Alternative data is a powerful tool, but resource constraints and legacy issues can make using it a daunting task. We look at some of the challenges adopters of alternative data face, and how solutions such as TransUnion’s CreditVision™ are essential to helping them leverage this new source of predictive power.
The use of trended and alternative data is the biggest shift we’ve seen in our industry since the introduction of credit scores. Where trended data looks at a consumer’s behaviour and repayment patterns over time, alternative data finds non-traditional sources of information which lenders can use to gain insights for improved decision making.
In the US it has been legislated that all credit bureaus must be able to introduce alternative data to their models. Locally, we are also seeing a big mind shift in major industries that are starting to understand the competitive edge alternative data offers. This is important for financial inclusion, as alternative data makes it possible for lenders to assess and access previously untapped markets.
But while we see a lot of innovation in the client onboarding and acquisition processes across the retail, banking and auto industries, there still is little to no innovation in scoring processes. If alternative and trended data are the way of the future, then why aren’t we seeing higher adoption rates of credit scoring based on this new tool?
The ability to take alternative data and use it to infer somebody’s credit behaviour demands a whole new level of capability. If an organisation wants to really apply alternative data, it needs the right technology to process structured and unstructured data (and apply the right algorithms), and it needs to have access to knowledgeable analysts who can scrutinise this new data to pick up on trends and correlations. All this needs to be done before they start building a credit model from non-credit data.
This isn’t something that happens overnight, or even in a few months. From developing to testing to credit-committee vetting, implementing a new model is a massive undertaking. When it comes to something like alternative data, where the in-house capabilities simply don’t exist, it’s understandable that it’s difficult for lenders to be agile in this space.
For lenders, a good credit model is all about predictability, so implementing a new alternative data model without the right resources is not only time consuming, but also very risky.
In addition to these resource constraints, lenders are under growing pressure to use credit data as a dialogue enabler. Consumers are becoming increasingly empowered: they want to be more in control of their financial wellbeing—including their credit health—and want to have meaningful engagements with lenders.
It is important for lenders to understand a very specific shift happening in the consumer market. On the one hand, you have the more mature market that doesn’t want people to have access to their private information. They are nervous about the fact that institutions have so much access and are scared about what the data is being used for. Here, lenders can use tools such as a TransUnion credit report to help their customers understand and even improve their credit history.
Then we see a newer market segment of consumers who are not afraid to share personal data; on the contrary, they have grown up in a world where making your data available is the norm. Whether it is through social media or other online platforms, as long as they get some form of return for sharing their data, they are happy. This could be in the form of connecting with certain circles, receiving special offers via targeted marketing or even just impressing their friends.
This is where alternative data can be harvested to inform processes across the customer lifecycle. From identifying and acquiring specific customers, to engaging with them in a way that is relevant, having access to alternative data benefits more than just your credit scoring capability: it also empowers you to have more meaningful conversations with a larger, and often untapped, group of consumers.
As much as large corporates in major industries know they need to use alternative data, they are still constrained by certain regulations and processes which, although there for a good reason, make it painfully slow to successfully take a reliable solution to market.
TransUnion’s CreditVision™ solution addresses both the resource and reliability issues organisations can run into when trying to adopt alternative data. We are part of a global organisation that has developed and deployed this model in Colombia, the US and Canada, so we’re able to leverage its knowledge, people and technology to develop solutions suited to the South African market. When compared to more traditional data models, trended and alternative data models have shown an overall increase in risk predictability of 56%.
Through CreditVisionTM we identified that, at any point in time, there were three million South African consumers who could not be scored using traditional credit data but were well-performing consumers when they did become credit active. CreditVisionTM can enhance our ability to assess these so-called thin-file consumers. Our solutions also incorporate useful demographic information that helps to assess those whose credit files have little or no data. Both lenders and consumers benefit as more consumers can be assessed, more accurately, more people have a fair chance of gaining access to credit on terms that align more squarely with their level of risk.
The simple (and exciting) fact is that alternative data is here to stay. Customers are becoming increasingly tolerant and accepting of alternative data, so the sooner lenders leverage the predictive power it offers, the sooner they can use new and varied data sets to inform not only their credit decisions, but their entire value chain.