Banking & insurance
Every prediction explainable down to the variable.
White-box models for credit, fraud and retention, built on your own infrastructure.
Fast by design
Full network data reconciled in hours, not days.
Data stays put
On-premise or private cloud. Data never leave your infrastructure.
Built for scale
Processes billions of records a day on a standard server.
Our approach
Built for what industry actually needs.
Anatella | Data prep engine
Connect to any sources
Core banking systems, policy administration platforms, balance sheets and transaction logs, joined without a data warehouse project.
Processes at portfolio scale
Millions of accounts and policies, with tens of thousands of derived variables, recalculated in full, not sampled.
One pipeline, one server
No cluster, no cloud migration. Runs within your existing infrastructure.
Modeler | Predictive Engine
Automatic variable selection
Tens of thousands of candidate variables, reduced to the ones that actually predict default, churn or withdrawal.
Algorithms competing mathematically
Several models benchmarked against each other, the best retained on statistical merit.
Built-in Drift Report
Flags a risk or conversion model losing accuracy as portfolios or market conditions shift.
SOLUTIONS
What that looks like in production.
Campaign conversion rate up ×4, to 6.2%
Broad, low-precision targeting reworked into a precision acquisition model for consumer credit.
Leasing conversion rate up from 4% to 34%
Product-specific targeting models replaced broad campaigns for high-end leasing offers.
98% accuracy on default risk scoring
BNP Paribas identifies companies at risk of bankruptcy using 24,000 financial trend variables.
Professional client attrition cut by 20%
A retention model flagged at-risk clients early, saving 7,000 accounts a year.
Net margin up 21% on business credit
TIMi identified professional clients with financing needs before competitors did.
€250M in savings kept invested, not withdrawn
An individual-level model showed AXA’s €4.5B savings product was more stable than assumed.
Problems solved
What banks and insurers fix with TIMi.
Averages instead of individuals
A single risk score applied to a whole segment misses the professional client about to default, or the one who never will.
Broad reach, narrow results
Casting a wide net still means contacting thousands of prospects or policyholders to convert a handful.
Caution instead of evidence
Without a reliable model of actual withdrawal or default behavior, funds sit idle rather than being put to work.
Signal buried in raw data
Balance sheets, transaction histories and claims records contain the signal, but only after being turned into the right variables.
FAQ
Common questions.
What is TIMi used for in banking and insurance?
Banks and insurers use TIMi for credit risk scoring, campaign targeting, client attrition prevention, and liquidity or capital modeling. Deployments include ING, BNP Paribas, Cofidis and AXA.
Can TIMi reduce client attrition?
Yes. For ING, TIMi cut professional client attrition by 20%, saving over 7,000 accounts a year.
Can TIMi predict business bankruptcy risk?
Yes. For BNP Paribas, TIMi reached 98% accuracy predicting which business clients would go bankrupt within 6 months, using 24,000 variables derived from public balance sheets.
Can TIMi help free up capital without increasing risk?
Yes. For AXA, TIMi’s individual-level withdrawal model showed that €250 million in additional funds could be invested while keeping full liquidity.
How much can TIMi improve campaign conversion?
For BNP Paribas, campaign conversion rose 8.5 times (from 4% to 34%). For Cofidis, conversion rose 4.6 points, generating an additional €3.5M in revenue.
Can TIMi run on-premise for a bank or insurer?
Yes. TIMi runs on-premise or in a private cloud, on standard server hardware, with no cluster required.
SEE FOR YOURSELF
Don’t take our word for it.
Run TIMi on your own data with a free trial or request a live demo.
