Understand Your Customers, Take Action: How to Use our Updated Attributes Engine
When we developed Attributes almost a year ago, we realized we could output an endless variety of insights. (Curious how it works? Head this way.) But if you can know everything, what do you focus on?
That’s the problem we fell in love with: providing the right insights, in the right format, so you can understand your customers and take action in your context.
In that spirit, we’ve been working on a major update to our Attributes engine: with new insights and better visualization tools, it’s easier than ever to use actionable insights — let’s take a closer look.
If you’ve been following our progress with data enrichment, you know that accessing financial data is just a start. The important — and challenging — part is to put it to use: turning a consumer’s raw transaction history into underwriting decisions, product recommendations, fraud prevention, and a myriad of other use cases.
New value is generated when financial data leads to actions with meaningful outcomes.
Ever since we launched Attributes last year, we’ve been listening closely to our clients to better understand how we can make it quick and easy for you to use transactional data.
Imagine you could start using actionable insights as soon as a customer connects their bank account.
I’m really excited to share our latest update: new insights to improve current use cases and unlock new ones, delivered in the format that fits your needs.
How to use our updated Attributes engine
All of these improvements can be seen in our newly designed User Analysis report, but keep in mind that everything you see here is also available via direct API calls. Here’s a quick breakdown.
New efficient design
The first thing you’ll see when you open reports in your Portal is a brand new design. It’s modeled after a traditional dashboard design, which makes it easier to filter through important information.
We’ve added handy hover boxes that provide definitions of each attribute, so that your team members always have a reference point for the meaning of the information they’re seeing.
Furthermore, you can now generate a User Analysis report that allows you to access and visualize data for multiple use cases in just a single document. In order to do so, these use cases are split by sections within the report.
More and better insights
Section #1 — KYC
The first section details standard identifying information for your customer, such as KYC and the request information. We also give visibility as to how far back we detect transactions within the account.
Section #2 — Income verification
This section enables you to determine how much your customer earns, and where this money actually comes from. We’ve made some data science advancements that provide more visibility on key information your team needs.
For instance, we now pull back the employer’s name, so that you no longer have to refer to statements to confirm where the payroll income comes from.
We also adjusted the employer income trend to now display a textual response (increasing, decreasing, constant), so your team doesn’t have to interpret percentages.
Finally, we added a pie chart to quickly scan for income sources (see above), as well as a full breakdown of government income sources — which was a heavily requested feature.
Section #3 — Credit risk analysis
As my colleague Vlad puts it, being able to determine creditworthiness might have become harder than ever. We updated our Attributes engine to more accurately capture all the data points you need for your risk underwriting.
The main addition is a breakdown of loan payments by categories, which is a key component of your liability analysis.
We now also return information on other loans your customer might have, including the name of those other lenders.
And finally, we’ve also added functionalities regarding cash flow trends in the account, so your underwriters have all information available at their fingertips to make a decision.
Section #4 — Fraud and primary account detection
Fraud detection can sometimes feel like looking for a needle in a haystack. To help with initial fraud detection, we’ve added an entirely new section where we utilize our Attributes engine to pull back eight main factors within the account that could be indicative of a suspect account.
We’ve made it easier for your team to quickly scan an account for unusual activity and red flags by adding a color code based on standard business rules.
This major update to our Attributes engine is part of what we do to help you digitize your processes faster, and with less resources.