What good analytics looks like in financial services
Financial services businesses sit on some of the richest data of any industry — and still struggle to answer basic performance questions.
The data is already there
Insurers hold policy, claims and underwriting data going back years. Asset managers hold detailed transaction and client data. Brokers hold placement, renewal and commission history. Almost no financial services business has a data shortage. What's usually missing is the layer that turns that history into a decision.
Questions the data should already answer
Which book of business is actually profitable once claims, commissions and acquisition costs are properly allocated? Which segments are driving loss ratio up — product, region, channel, or underwriting vintage? Which clients or policies are likely to lapse or churn, and is that predictable early enough to act on? Where is commission or fee leakage happening between what was agreed and what was paid? These are answerable questions. They're just rarely asked in a structured way.
Why this is harder than it looks
Financial services data has its own logic — earned versus written premium, incurred but not reported claims, regulatory reporting periods that don't match management reporting periods. A generic analytics approach that ignores this logic produces numbers that look precise and are quietly wrong. This is where financial training and analytical technique need to sit together, not in separate teams that hand work back and forth.
What a focused engagement looks like
It rarely needs to start big. A profitability view by segment. A churn or lapse model built on data the business already has. A monthly management pack that surfaces what changed and why, instead of forty pages management has to interpret themselves. Start with the question that would actually change a decision this quarter, and build outward from there.