Thick Data: A New Weapon to Thwart Fraud
As smart as they are, fraud detection algorithms fall short when hunting down credit card theft.
They red-flag potentially suspicious purchases such as, say, an order for 10 shirts in size L. But they typically miss subtler techniques used by fraudsters, such as an order for two shirts in every size, or a delivery address that’s an unoccupied home for sale.
How do we know card thieves’ tactics? We asked them.
While big data excels at sketching broad outlines, so far it has been unable to thwart rapidly growing credit-card fraud. Adding human insight to the mix, however, fill in the details.
To explore how the one-two combination of big data and human insights can level the playing field, we conducted a proof of concept and applied the pairing to credit card fraud.
Human insight is the job of so-called thick data, the kind of qualitative, real-world details we uncovered during our conversations with fraudsters. When overlaid on big data’s bold strokes, thick data helped spot tell-tale signs of card theft — and turned up potential new weapons to use against it. That’s good news for industries that are hard hit by the crime, such as banking and financial services and retail.
When it comes to cataloguing what people do, big data is unrivalled in its ability to reveal records (contained in structured formats) of individuals’ financial transactions and online activities, not to mention their tweets, likes, and pins from unstructured and semi-structured containers.
To augment the sea of data, organisations typically turn to focus groups and surveys. That’s a start. But thick data goes one better: Instead of relying on individuals’ opinions and ability to recall, it observes firsthand how people act and experience the world. We refer to it as contextual analytics.
Traditionally the purview of sociologists and anthropologists, thick data is finding a new role in the enterprise as social science’s importance to digital success – and its role in interpreting digital data — becomes clear. Our partner ReD Associates penned a seminal article (subscription required) about thick data’s power to understand customers’ emotional lives. Thick data’s possibilities intrigue IT leaders.
But the goal is more than building a better algorithm. How can organisations combine thick data and big data to tackle tough business challenges?
To find out more, we explored credit card fraud. The stakes are high. Banks and retailers lost $16 billion to credit card fraud in 2016, and scammers claimed two million more victims than in 2015.
Worse, fraudsters move almost ghost-like through the dark web. Financial services providers and credit-card facilitators see only the traces that perpetrators leave in data, the patterns of their transactions. They know little about what motivates them, or what might scare them away.
We wondered what financial institutions might learn if data scientists could gain a firsthand understanding of the world of credit card fraud looks like. Could they build better fraud detection algorithms?
The question triggered months of study for our proof of concept. We met, conversed with, and observed fraudsters — and garnered surprising results. Credit card thieves see their line of work as hustling to survive, not a get-rich-quick play. Longevity requires they move fast and spend small sums of money to stay below the radar. It’s labour-intensive work.
That reality opens a different window on how to deter them: Make their work more difficult, and suddenly it’s a less attractive option.
By taking into account the human perspective, banks can sharpen their fraud-detection algorithms. They can better spot the types of orders fraudsters place. They can scrape real-estate databases for-sale listings and cross-tab them against credit-card transactions and produce potentially suspicious purchases.
Big data, it seems, benefits from a human touch.
What business solutions can it team up most effectively with thick data to solve? Any application of analytics that looks to examine and take into account underlying behaviours, for example, call-centre dynamics and customer acquisition and retention.
Take a mortgage processing company that acquires loans from large banks. To increase customer loyalty and refinancing opportunities, it might pair thick data and big data to understand the drivers of retention. It might also use it for internal purposes, such as determining the characteristics of successful loan officers. What findings can it turn up to hire high-performing loan officers?
One challenge for organisations is putting clearly defined processes in place. That is, which queries are best posed against thick data? Which against big data? It’s common to try to answer the wrong question with the wrong method.
For example, because most organisations have grown accustomed to associating data with numbers, they often expect to analyse big data first, and then move to thick data. But numbers can constrict their vision. Thick data is where you start developing questions and theories, and then move to big data to scale them.
This article was written by Poornima Ramaswamy, a Vice President within Cognizant Digital Business’s Analytics and Information Management Practice, and Mikkel Krenchel, a Partner with ReD Associates. It was previously published on Cognitive Perspectives.