Tag: Decisioning
In this latest installment of “working with vendors” let’s dive into some best practices for writing RFIs and RFPs.
Digitalization continues to remain a top priority for many organizations in 2021.
If you’re looking to buy new decisioning software, your first inclination might be to issue an RFI or an RFP. However, that may not be the best idea.
Time is the only resource we can’t get more of, which is why we obsess over saving it. Over the last several years, a new time saver has emerged – APIs.
Our Attribute Toolbox provides maximum flexibility and multiple data sources you can use in the calculation and management of attributes.
Digital transformation is the new business model, and its powered by data and the insights gleaned from data.
The 2019 report reveals new consumer and lending insights, an overview of the regulatory landscape, and what's next for machine learning and alt data.
You want to use big data, but how do you make your analytics truly actionable to stay ahead of the competition? Using an analytical sandbox is the answer.
There are a lot of people talking about big data who are not fully leveraging the value of their data. How do you use data to innovate and stay competitive?
Once a scorecard has been redeveloped, it is important to measure the impact of changes within the strategy by replacing the old model with the new one. This impact assessment can be completed with a swap set analysis.
With 81% of Americans having a social media profile, you may wonder if social media insights can be used to assess credit risk. When considering social media data as it pertains to financial decisions, there are 3 key concerns to consider.
Later this year, FICO will retire its Score V1, making it mandatory for those lenders still using the old software to find another solution.
Key drivers to auto financial services are speed and precision. What model year is your decisioning system? In the auto world the twin engineering goals are performance and durability. Some memorable quotes have been offered about the results of all that complex engineering. And some not so complex observations. The world of racing has offered some best examples of the latter. Here’s a memorable one: “There’s no secret. You just press the accelerator to the floor and steer left. – Bill Vukovich When considering an effective auto financial services relationship one quickly comes to the conclusion that the 2 key drivers of an improved booking rate is the speed of the decision to the consumer/dealer and the precision of that decision – both the ‘yes/no’ and the ‘at what rate’. In the ‘good old days’ a lender relied upon his dealer relationship and a crew of experienced underwriters to quickly respond to a sales opportunities. Well, these days dealers will jump to the service provider that delivers the most happy customers. But, for all too many lenders some automated decisioning is leveraged but it is not uncommon to still see a significantly large ‘grey area’ of decisions that falls to the experienced underwriter. And that service model is a failure of speed and precision. You may make the decision to approve but your competition came in with a better price at the same time. His application got booked. Your decision and the cost incurred was left in the dust – bin. High on the list of solutions to this business issue is an improved use of available data and decisioning solutions. Too many lenders still underutilize available analytics and automated decisions to deliver an improved booking rate. Is your system last year’s model? Does your current underwriting system fully leverage available third party data to reduce delays due to fraud flags. Is your ability to pay component reliant upon a complex application or follow-up requests for additional information to the consumer? Does your management information reporting provide details to the incidence and disposition of all exception processes? Are you able to implement newer analytics and/or policy modifications in hours or days versus sitting in the IT queue for weeks or months? Can you modify policies to align with new dealer demographics and risk factors? The new model is in and Experian® is ready to help you give it a ride. Purchase auto credit data now.
As Big Data becomes the norm in the credit industry and others, the seemingly non-stop efforts to accumulate more and more data leads me to ask the question - when is Big Data too much data? The answer doesn’t lie in the quantity of data itself, but rather in the application of it – Big Data is too much data when you can’t use it to make better decisions. So what do I mean by a better decision? From any number of perspectives, the answer to that question will vary. From the viewpoint of a marketer, maybe that decision is about whether new data will result in better response rates through improved segmentation. From a lender perspective, that decision might be about whether a borrower will repay a loan or the right interest rate to charge the borrower. That is one the points of the hype around Big Data – it is helping companies and individuals in all sorts of situations make better decisions – but regardless of the application, it appears that the science of Big Data must not just be based on an assumption that more data will always lead to better decisions, but that more data can lead to better decisions – if it is also the “right data”. Then how does one know when another new data source is helping? It’s not obvious that additional data won’t help make a better decision. It takes an expert to understand not only the data employed, but ultimately the use of the data in the decision-making process. It takes expertise that is not found just anywhere. At Experian, one of our core capabilities is based on the ability to distinguish between data that is predictive and can help our clients make better decisions, and that which is noise and is not helpful to our clients. Our scores and models, whether they be used for prospecting new customers, measuring risk in offering new credit, or determining how to best collect on an outstanding receivable, are all designed to optimize the decision making process. Learn more about our big data capabilities
In today's data driven world, decisioning strategies can no longer be one-dimensional and only risk-focused. By employing a multidimensional decisioning approach, companies can deliver the products and services customers need and want.