KreateIQ AI-Assisted Trade Reconciliation and Settlement Intelligence for Capital Markets

About the Project

The client is a full-service custody and securities services bank with an active proprietary trading book and institutional client execution business spanning Indian equities, fixed income, and government securities. Its settlement footprint runs across NSDL and CDSL for domestic equities and corporate bonds, CCIL for government securities, and a network of correspondent banks for cross-border instruments , alongside a securities lending and repo book that demands daily, multi-counterparty reconciliation.

Kreate Technologies was engaged to design and deploy KreateIQ, an AI-driven trade reconciliation and settlement intelligence platform, with one mandate: replace a settlement operation built entirely for reactive response with one engineered for prediction and prevention.

The Challenge

Every existing tool the bank ran , confirmation queues, exception reports, end-of-day reconciliation , was calibrated to surface problems only after they had already become problems. As one operations leader put it: By the time we found out a trade was going to fail, it had already failed. , Head of Post-Trade Operations, Custody and Securities Bank Three forces compounded the problem:

Challenges

A T+1 settlement clock that left no margin for error. What two business days could absorb under T+2 now had to be resolved within a single day , sometimes within hours for afternoon-executed trades.
Five recurring, distinct root causes of failure. Pre-settlement matching breaks, securities availability gaps, intraday funding timing mismatches, instruction format exceptions, and corporate-action complications , each needing a diff erent fix, none flagged until too late.
No system built to look forward. Funding shortfalls were discovered only once the settlement window opened, by which point there was no practical path to resolution.
A scale problem no headcount could solve. The volume and velocity of trades crossing the pipeline each day exceeded what any human-staff ed monitoring process could track continuously.

Our Solution

Kreate Technologies built KreateIQ around a simple hierarchy: predict before you prevent, prevent before you resolve. Rather than waiting for a settlement instruction to bounce, the platform generates a continuous, forward-looking risk picture across the entire trade pipeline , trained exclusively on the bank's own historical settlement data so it learns the patterns specific to its counterparties, market mix, and operating conventions.

Deployment ran across five phases over twelve months, expanding in scope and asset coverage at each stage:

Phase 1 , Foundation (Months 1–2)

Settlement infrastructure connectivity established and validated; historical data ingested for model training; trade pipeline data model designed.

Phase 2 , Calibration (Months 3–4)

Fail prediction model trained and validated against known fail cases; pre-settlement matching intelligence live in monitoring mode; operations team familiarisation underway.

Phase 3 , Activation (Months 5–6)

Fail prediction engine live with daily pipeline scoring; position intelligence deployed; platform adopted as the primary workflow tool for equity settlement.

Phase 4 , Expansion (Months 7–9)

Fixed income and government securities brought onto the platform; repo and lending module deployed; intraday funding intelligence integrated into treasury workflow.

Phase 5 , Maturity (Months 10–12)

Corporate action overlay fully deployed; counterparty intelligence model live; cross-infrastructure coverage complete; first performance audit and model refinement cycle run.

Results

Business Outcomes

The shift in orientation was as significant as the numbers it produced. Settlement operations moved from afternoon firefighting to morning forecasting , expert judgment applied while there was still time to act on it, rather than once the damage was already done.
Fail awareness timing — Before: At settlement deadline, after crystallisation | After: Hours ahead, via AI prediction
Pre-settlement break detection — Before: Manual confirmation monitoring | After: Continuous automated monitoring, directed resolution
Securities position gaps — Before: Reactive, once visible | After: Predictive, risk-adjusted forward projection
Intraday funding shortfalls — Before: Discovered at window open | After: Projected ahead of settlement day
Counterparty follow-up — Before: Uniform across all confirmations | After: AI-prioritised by behaviour model
Corporate action impact — Before: Found during settlement or post-fail | After: Mapped in advance, at notification
Team effort allocation — Before: Concentrated on reactive resolution | After: Projected ahead of settlement day

Operational Impact

The gains compounded rather than simply added up. Fewer pre-settlement breaks meant fewer unintended open positions; fewer open positions meant fewer downstream securities availability gaps; tighter funding visibility meant fewer tertiary fails cascading from earlier purchase failures. Each module's benefit amplified the next.
Before the platform, our best people spent their afternoons managing failures. After the platform, they spent their mornings preventing them. Prevention is a better use of their skill than resolution , and they found it signifi cantly more satisfying. , Managing Director, Custody and Securities Services
The improvement in settlement reliability was visible externally too: the bank's most active counterparties noticed, strengthening custody relationships and reinforcing its reputation as a dependable settlement counterparty.

Key Takeaways

Train on your own history, not industry proxies. Settlement fail patterns are specific to a bank's counterparty mix and infrastructure , generic models miss what actually drives failure in any one operation.

Couple prediction with position modelling. Linking fail probability to forward position projections let KreateIQ catch secondary and tertiary fails before they cascaded , not just the primary trigger.

Give the trading desk a seat at the table. Routing high-severity alerts to traders, with commercial impact quantified alongside operational risk, unlocked resolution levers operations alone could never pull.

T+1 makes prediction a necessity, not a nicety. Compressed settlement windows leave no room for discovery-after-the-fact , by the time a reactive process flags a problem, the deadline has often already passed.

Prevention is a better use of expert judgment than recovery. Shifting skilled staff from afternoon damage control to morning risk triage improved both outcomes and morale.