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Fundamentals of analytic learning loops

My last few posts have discussed the analytic learning loop. By accelerating feedback about market performance, analytic learning loops enable banks to target the right products to consumers most likely to respond and generate profits. And banks can adjust decision strategies to boost results while campaigns are still ongoing.

So what does this approach look like?

The fundamental component of an analytic learning loop is an analytic learning hub, through which insights from data analysis and learning from production testing flow. This hub will generally be comprised of analytic data marts (continually refreshed from internal and external data sources), a repository for past treatments, a variety of analytics to monitor what is happening as well as project what might yet happen, and a methodology for creating actionable diagnostic reports.

As shown in the graphic, the learning loop is then "threaded" through both acquisitions and originations processes. This "threading" occurs through shared access to the hub as well as automated data feeds from operational systems, bringing decisioning results and account performance outcomes back to the hub.


The open, hub-based approach is a far less costly and disruptive way to improve visibility and coordination between acquisitions and originations than traditional one-to-one systems integration.

Of course, analytic learning loops are not limited to acquisitions and originations. One of the benefits is that banks can put learning loops in the area of highest priority—which for most companies today is front-end growth—then expand them by hooking other decision areas into the hub. They can also extend learning across lines of business. Or scale analytic learning loops to serve acquired portfolios and new geographic or demographic markets—a practical way to promote best practices, and achieve decision consistency and cost efficiencies across the enterprise.

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