How are advances in artificial intelligence and machine learning changing credit risk assessment? That’s a topic I’ll be exploring in three presentations at FICO World 2018.
Want to know which consumers will more likely default when they encounter an unpredicted financial stress situation in the future? Traditional risk scores won’t give you the full picture.
As analytic advancements reach ever deeper into people’s lives — as every aspect of individuals is analyzed to drive decisions by businesses and other institutions — the need for people to trust analytics and the way organizations use them grows. This issue arose in a recent announcement from the industry analyst firm Gartner. Gartner ”believes the trust factors influencing the ethical use of analytics are identifiable — transparent, accountable, understandable, mindful, palatable and mutually beneficial” and that “leading data-driven organizations will increasingly recognize the causal relationships between data, analytics, trust and business outcomes.” This made me reflect on some lessons I’ve learned as a data scientist as they relate to the interplay between data, models, trust and results. I will omit here some of the more obvious topics such as model validation and tracking model performance. I find it beneficial to think about analytics as an element of a virtuous... [Read More]
At the Credit Scoring and Credit Control XIV conference August 26-28, I will be discussing innovative analytic techniques and applications revolving around engaging fickle customers and tackling silent attrition. Here is a preview of my talk: Everyone knows the ideal customer: frequent shopper, highly profitable, engaged, loyal. But in today’s competitive markets the most profitable customers are a fickle species and can turn away (or more likely are lured away) on a dime, possibly without giving prior warnings. “Silent attrition” happens when customers stop transacting without saying “I’m no longer your customer”. It occurs in non-contractual relationships such as in retail and it happens with credit cards when customers stop using a card without canceling it. When silent attrition emerges, businesses have a brief window of opportunity to try to re-engage the customer before the parting becomes cemented. Rapid detection of silent attrition and fast contact through mobile channels provides... [Read More]
By Dr. Gerald Fahner Today, debates are raging in blogs as to whether Big Data and machine learning render domain expertise obsolete. Personally, I do not think it is an either or decision. I like to design effective processes and tools to transfer critical domain expertise into algorithmic models. FICO has developed an innovative approach to develop better, business-apt scorecards faster, and we have been invited to present our work as a semifinalist for the INFORMS Innovation Award. Credit scoring serves as a perfect use case for combining brute force machine learning algorithms (which are all about fitting models closely to historic data) with domain expertise (all about tuning models to the context into which they are being deployed – respecting business goals and constraints.) But first, what is algorithmic learning, and why is it so powerful? Algorithmic learning comprises a large body of theories and algorithms, and has many subfields beyond the scope of this blog. From the perspective of scoring, well-known, readily available tools and procedures...
By Dr. Gerald Fahner During the dawn of commercialized analytics, in 1958, FICO applied predictive modeling to credit decisions. In the 90’s Edward Lewis wisely instructed credit professionals not to interpret dependencies captured by scoring models as causal effects: “Causality is a dangerous myth” [1] (pg.1). Kenneth Cukier and Victor Mayer-Schönberger described [2] how non-causal (associative, correlational) models are quickly becoming pervasive as they nourish on the cornucopia of Big Data – recent examples include epidemics prediction from internet searches and product recommender systems. In their own words “finding associations in data and acting on them may often be good enough.” (pg. 49) Echoing Lewis’ advice, they rightly warn not to insinuate causal connections from mere correlational findings. Going further, they pronounce “experiments to infer causal connections are often not practical or raise challenging ethical questions” and they conclude “Big data turbocharges non-causal analyses, often replacing causal investigations.”...
By Dr. Gerald Fahner In the Harvard Business Review article “Good Data Won’t Guarantee Good Decisions,” Shvetank Shah, Andrew Horne and Jaime Capellá argued that “Big Data” won’t live up to its promises to lead to better business decisions, unless complemented with educated human judgment. It divided decision makers into three groups: Unquestioning empiricists who trust data analysis over judgment Visceral decision makers who rely on gut feeling Informed skeptics who balance judgment and data for their decisions My clients and associates, who use predictive analytics to improve business decisions, tend to identify with the third group. Although we may at times feel attracted to one or the other extreme position, depending on the type of problem and data conditions. While the article didn’t go into the guts of predictive analytics, there are obvious relations to practices for developing predictive models for consumer behavior. Various modeling decisions have to be made, including choice of a model type, data transformations, predictor...