Fintech
Data for digital lending and financial services
Fintech data, from the inside
My production career has been inside fintech: digital lending, credit scoring, and the data that risk teams, finance teams and partner institutions depend on every day. I know what a repayment schedule, a days-past-due bucket and a credit decision need from a data platform, and what goes wrong when the platform cannot supply it.
How I work with fintech teamsQuestions fintech teams bring
- Can we trust today's portfolio-at-risk number?
- Only if the pipeline proves it before the dashboard shows it: contracts on the inputs, reconciliation against the source, and lineage that shows where every figure came from.
- How do we add a new lending partner without building a new pipeline?
- Standardise the model once, then make each partner a configuration: which door it uses (CDC, extract or dump), its schedule and its field mapping.
- Which features can the credit model use in production, and how fresh are they?
- The ones defined in the feature store with an online path and a refresh schedule. Anything computed only in a notebook is not a production feature yet.
- How do we join on personal data without exposing it to analytics?
- Join on a keyed hash, keep the raw identifier encrypted in a restricted schema, and publish masked marts. Analysts get the joins, not the identities.
- We have monthly batch reports. Do we need real-time?
- Usually hourly change data capture with exactly-once loads is the right step. It removes the multi-hour batch window without the operating cost of a streaming cluster.
The language I work in
The domain terms that come up in every lending data conversation, and that the platform has to get exactly right.
- Loan origination
- Disbursement
- Repayment schedules
- Days past due
- Portfolio at risk
- Credit scoring
- Credit decisioning
- Alternative credit data
- KYC and identity
- Partner institutions
- Reconciliation
- Data contracts
- PII masking
- Audit trails
Building a fintech data platform?
Open to Data Architect and Senior Data Engineer roles, and to selected consulting engagements in data architecture, governance and engineering.