Decisioning – open data use cases

In this edition of open data use cases, we look at how permissioned data can power a business’ capability to accelerate decisioning engine. This is a use case most fitting of tertiary (service) industries like insurance and financial services. The value of accelerated and transparent decisioning, enabled by open data, broadly falls under two buckets: speedier service delivery paired with a better customer experience and the diversification of data sources that feed a decisioning engine to minimize risk exposure.

 
What is the role of decisioning engines?

Decisioning processes drive three primary goals:

  1. Qualify customers for their suitability to a product or service at speed
  2. Accelerate service delivery and improve the customer experience
  3. Minimize the risk a business faces by diversifying the data sources feeding a decisioning engine
 
How does open data power decisioning engines?

A key tenet of open data is the connectivity between different datasets hosted in different places. If a business wants to source specific datasets to feed into their decisioning engine, there are two steps they need to take:

  1. Ensuring the connectivity of the dataset with the decisioning engine.
  2. Gaining the consent from customers to use their datasets as part of the decisioning process.

Utilizing open data creates a seamless experience for customers, manifested as a fast, checkbox-type interaction in most digital experiences. The result is that high-quality data is sourced in real time from customers’ data platforms and business apps to feed into decisioning.

Business models between the different players in the service sector — for example, retail, banks, computer services, recreation, media, communications and more — will naturally differ. Unique datasets that are optimal for each service provider is required, to feed into decisioning engines to minimize the risk individual businesses face. For example, a life insurance provider can minimize risk by feeding not only diversified datasets into the model — but equally important is pulling specific or relevant customer data on a case-by-case basis to provide a personalized service. The underwriting models between life insurance providers also likely varies. As such, even between similar businesses, the data requirements for insurance applications may differ.

 
Open data-powered decisioning engines in banking and financial services

Decisioning at speed and with high accuracy has become a fundamental requirement for banking and financial services, especially as customer expectations evolve. Decisioning usually occurs at the point of application for a financial product. The typical case is either a loan application is rejected or approved. Assuming the latter occurs, a loan agreement is drawn up with the service provider and funds are transferred to the customer. What follows is often a passive loan monitoring program that involves manual checks and ad-hoc follow ups to ensure the banks’ investment is performing as expected. In this model, the customers’ ability to repay is only assessed once, when they apply. With open data, however, financial institutions can dynamically assess their portfolio risk on an ongoing basis and adjust if needed.

Brex, a provider of business credit cards and cash management software, entered and disrupted the market in 2018 with a dynamic underwriting model (Harvard Business School, 2019). Brex assessed the risk amongst its portfolio of business credit card holders every single day by leveraging real-time data. What this means for Brex customers is that the interest rate on their credit card reflects the project financial health of their business — determined by Brex’s underwriting model. If key metrics or ratios are trending negatively, that will reflect in the next day’s assessment. In contrast, other providers of business credit cards don’t re-assess their portfolio risk daily, and therefore, incur higher risk. Brex’s model is made possible by open data — such as bank APIs that connect to current account feeds. As more financial datasets become freer and easier to integrate with, it would be no surprise if other market players adopted Brex’s approach to dynamic underwriting. In essence making decisioning a daily process.

 
Powerful and flexible decisioning benefits businesses and customers alike

Imagine, as a consumer or a business representative, you can find out whether you qualify for a financial (or otherwise) product or service within seconds. Long gone will be the days of having to email through documents of different financial (or other type) datasets, as requested by the service provider. The only action an applicant will need to take is to consent to connecting their banking and business apps — where that data already lives. The benefits to the customer are clear – the ability to acquire application approvals at speed and with minimal fuss. The upside for the business is that they can decision quickly, meet the expectation of instant service, and importantly, reduce risk by making the right offers to the right customers.  

 
References

PF, Tommy. (2019). A Brex of Fresh Air in Corporate Credit Access. Harvard Business School. https://digital.hbs.edu/platform-digit/submission/a-brex-of-fresh-air-in-corporate-credit-access/

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