Use case · Financial Services
Reconciliation in financial services
Automating reconciliation where every automated decision must be explainable and reproducible months after the fact.
- Sector
- Financial Services
- Systems involved
- 5
- Regulations in scope
- 5
What makes this hard
In financial services, every automated decision must be explainable and reproducible months after the fact. Applied to reconciliation, that means the automation has to carry an audit trail and a clean escalation path before it carries any speed benefit at all.
We build the smallest thing that proves the case, put it in front of real users, and expand only what earns its keep.
Built by engineers who ship production systems, not by a practice that subcontracts the build. Six weeks to something running in production, not six quarters to a strategy document.
How we sequence it
- 01BaselineMeasure the current cycle time, touch count and error rate on reconciliation. Without that number there is no way to prove the automation worked.
- 02Map the exceptionsDocument what actually happens when the process does not run cleanly. The exceptions, not the happy path, decide whether this automation survives contact with real operations.
- 03Integrate firstConnect to core banking and trading and OMS before building any intelligence on top. A model that cannot reach the system of record cannot finish the work.
- 04Ship narrowAutomate the highest-volume, lowest-variance slice and put it in front of real users, with anything uncertain escalated to a human.
- 05Measure and widenReport the straight-through rate against the baseline, then absorb the next tier of exceptions. Coverage rises over time rather than being promised on day one.
Context
- Workload
- reconciliation
- Sector
- Financial Services
- Sector constraint
- every automated decision must be explainable and reproducible months after the fact
- Systems of record
- core banking · trading and OMS · loan origination · SAP and Oracle financials · regulatory reporting platforms
- Regulations in scope
- RBI guidelines · SEBI regulations · DPDP Act 2023 · PMLA and AML rules · IRDAI where insurance applies
Capabilities that deliver this
Questions
Can reconciliation be automated reliably?
The high-volume, low-variance portion can, with anything uncertain escalated to a human. In financial services, every automated decision must be explainable and reproducible months after the fact, so the escalation path matters as much as the automation itself.
What does it integrate with?
Typically core banking, trading and OMS, loan origination, SAP and Oracle financials, regulatory reporting platforms. We assess your specific estate during discovery rather than assuming a standard setup.
What about compliance?
RBI guidelines, SEBI regulations, DPDP Act 2023, PMLA and AML rules, IRDAI where insurance applies are in scope for this sector. Audit trail and human oversight are built in from the start, not added before go-live.
Can we use AI in credit decisions?
With explainability, documented model governance and human review on adverse outcomes, yes. RBI expects you to be able to explain any decision that affects a customer.
How do you handle data residency?
Deployment inside Indian regions or on your own infrastructure, which is the usual requirement for regulated financial data.
Other financial services workloads
Automating reconciliation?
Bring us your current cycle time. We will tell you what is realistically removable.
Or email bd@dtrasglobal.com · call +91 74118 77878