In this video, detective Jesse Gossman shares how synthetic identities are used, and the risks associated with Credit Profile Numbers (CPNs).[Read More]
The mathematical optimization community already has a clear set of standards for MIP benchmarking - these should be followed.[Read More]
How are banks solving common challenges in acquiring and retaining new customers, from marketing to origination to onboarding; and how many of these processes are intertwined in delighting the customer?[Read More]
In this video, detective Jesse Gossman shares how synthetic identities are used, and the risks associated with Credit Profile Numbers (CPNs).
The mathematical optimization community already has a clear set of standards for MIP benchmarking - these should be followed.
In my role at FICO I have the privilege of engaging in strategic planning discussions with senior banking executives across the globe. Most of these meetings as of late are similar: We are improving the customer on-boarding user experience, both through improved user interfaces, but also through improved analytics to bring everything we know about the customer to bear in our interaction…regardless of product line or channel. Right now we are working on our data lake. At which point, I nod. Then I ask: OK, so who is in charge of the customer journey that you are transforming? At a strategic level, banks understand the challenge they are facing and where their organizations need to go in order to thrive. At a tactical level, the decisions become much more complicated. Who owns the customer? Is that new data source worth the risk of potentially slowing down my origination process? How seriously... [Read More]
My presentation on “Transparency by Design: An Explainable AI Approach to Credit Risk Scoring” discusses our application of recent machine learning technologies to challenge the US FICO® Score.
Pressures from internal finance departments reacting to accounting standards have strong implications for managing collection agencies and debt buyers.
“There are three kinds of lies: lies, damned lies, and statistics” – Attributed to Benjamin Disraeli, British PM (1874-1880) In assessing the predictive value of new scores or new data, significant weight is given to model performance measures. Examples of these measures — which quantify the effectiveness of a predictive model in distinguishing between the outcomes being predicted — include the Gini coefficient, KS (Kolmogorov-Smirnoff statistic), and ROC Area. These measures are appealing because they allow a straightforward means of comparing the relative effectiveness of two or more models. Think of them as scoring the scores — the score or data with the highest “score” looks like the best one to buy or use. Score and data vendors often structure their validations in a way that produces a more compelling case for their product. And sometimes their efforts fall into the grey area of gaming — producing misleading results by tweaking... [Read More]
I’m excited to welcome Network International to the Falcon Intelligence Network, the largest payments data source in the world, with data on more than 2.6 billion payment devices.
Our global survey with TM Forum demonstrates that many CSPs are at a relatively early stage of using advanced analytics in telecommunications — and European CSPs appear to be further behind.
AI and machine learning have become popular concepts in payment integrity. But how can they help reduce your losses to health care fraud, waste and abuse?
Through collection treatment optimization, you can determine the ideal action to take on every account, as well as when to take it.