Automation & Integration in New York

Financial Services / Investment Banking and Securities

A Wall Street investment bank (top 10 by trading volume, $2.8T in annual trade volume across equities, fixed income, and derivatives, 4,200 institutional clients) needed to automate its trade settlement operations for the T+1 settlement cycle while reducing operational risk and compliance exposure. (1) T+1 readiness: the SEC's move from T+2 to T+1 settlement halved the time available for trade matching, affirmation, and settlement. The bank processed 2.4 million trades per day — and the existing settlement workflow relied on significant manual intervention for exception handling, with 340 operations staff managing trade breaks, failed deliveries, and counterparty discrepancies. Under T+2, the bank had adequate time for manual resolution. Under T+1, manual processes would create systemic settlement failures. (2) Integration fragmentation: trade data flowed through 28 different systems — order management, execution management, middle office, risk, compliance, custodian connectivity, DTCC interfaces, and client reporting. These systems were connected through 180+ point-to-point integrations — many built over 15 years, maintained by different teams, and running on different technologies (MQ Series, FTP, custom TCP sockets, REST APIs, and SFTP batch files). (3) Exception handling was manual: 8.5% of trades required manual intervention — mismatched economic terms, missing SSIs (Standard Settlement Instructions), failed deliveries, and corporate action adjustments. Each exception required an operations analyst to investigate across multiple systems, identify the root cause, and apply the correction. Average resolution time: 4.2 hours per exception. (4) Reconciliation consumed massive resources: daily reconciliation across 14 custodians, 8 prime brokers, and internal books required matching millions of records — positions, cash, and transactions. 120 analysts spent their mornings reconciling, with 2.1% of items requiring investigation. Reconciliation had to be complete before settlement could proceed. (5) Regulatory reporting was error-prone: CAT (Consolidated Audit Trail), OATS, and trade reporting obligations required accurate, timely submission of trade data. Manual data assembly from multiple systems resulted in 0.3% error rate — small in percentage but representing thousands of erroneous reports per month that required correction filings.

We built a comprehensive trade settlement automation platform over 14 weeks. (1) Event-driven trade processing: Kafka-based event backbone replacing the 180+ point-to-point integrations with a centralised event streaming platform. Every trade lifecycle event (order, execution, allocation, confirmation, affirmation, settlement instruction, settlement confirmation) published to Kafka topics — downstream systems subscribed to relevant events rather than receiving pushed data through custom integrations. Real-time processing: trade events processed within milliseconds of occurrence — enabling T+1 settlement with time to spare. Event sourcing: complete trade lifecycle history maintained as immutable event log — enabling replay, audit, and investigation without querying multiple source systems. (2) AI-powered exception handling: ML classification of trade exceptions — trained on 3 years of exception resolution data (2.1 million resolved exceptions). The model classified each exception by root cause (SSI mismatch, economic term discrepancy, corporate action, client instruction error) and recommended resolution. Auto-resolution: for 6 of the top 10 exception types (representing 72% of all exceptions), the system could apply the resolution automatically — updating SSIs, applying corporate action adjustments, and correcting allocation errors without human intervention. Confidence scoring: each auto-resolution included a confidence score — resolutions above 95% confidence applied automatically, 80-95% applied with analyst notification, below 80% routed to analysts with the recommended resolution pre-populated. (3) Automated reconciliation: real-time reconciliation engine matching positions, cash, and transactions across all custodians and prime brokers continuously throughout the day (rather than batch overnight). Fuzzy matching: ML-based matching that handled the inevitable format differences between counterparties — different security identifiers, reference number formats, and amount rounding. The matching engine resolved 97.8% of items automatically (from 97.9% manual matching — but in real-time rather than overnight batch). Break investigation: automated root cause analysis for reconciliation breaks — the system identified whether the break was a timing difference, a genuine discrepancy, or a known corporate action effect before routing to an analyst. (4) Regulatory automation: CAT reporting (automated assembly and submission of CAT lifecycle events — trade data aggregated from the event backbone, validated against CAT specifications, and submitted within the required timeframes), OATS (automated order audit trail generation — complete order lifecycle from receipt through execution, routed through the order management system events), and trade reporting (automated submission to FINRA, MSRB, and TRACE — with pre-submission validation catching format and content errors). (5) Client communication automation: automated settlement status notifications to institutional clients — real-time updates on trade affirmation, settlement instruction status, and settlement confirmation. Client portal API: clients accessed real-time settlement status through API rather than calling operations staff for updates.

T+1 readiness: the bank achieved 98.7% same-day affirmation rate (the critical metric for T+1 success) — up from 73% under the manual process. Settlement fail rate dropped from 2.1% to 0.4% — the lowest among top-10 investment banks. The bank was cited by DTCC as an industry leader in T+1 readiness. Exception handling: auto-resolution rate of 72% (from 0% — all exceptions previously required manual handling). Average exception resolution time from 4.2 hours to 18 minutes (including the exceptions that still required analyst review). Operations staff redeployed: 220 of 340 operations analysts moved from exception handling to higher-value roles (client service, process improvement, new product onboarding). Reconciliation: from overnight batch (results available 9am next day) to continuous real-time (breaks identified within minutes of occurrence). Reconciliation analyst team from 120 to 35 FTEs — with the remaining team focused on complex break investigation rather than routine matching. Break resolution time from 3.5 hours to 42 minutes. Regulatory: CAT reporting error rate from 0.3% to 0.02%. Zero regulatory correction filings in the 12 months following implementation (from an average of 45 corrections per month). FINRA examination: the automated audit trail and data lineage capabilities specifically praised by FINRA examiners. Annual value: $128M (operational efficiency — 285 FTE reduction in operations and reconciliation teams at an average fully loaded cost of $180K per analyst, plus reduced overtime and temporary staffing), $45M (settlement fail reduction — avoided fail charges, buy-in costs, and counterparty penalties from 2.1% to 0.4% fail rate on $2.8T volume), $22M (regulatory risk reduction — eliminated correction filings, reduced examination findings, and avoided potential penalties), $18M (client retention — improved settlement performance and real-time transparency retaining clients who had been considering competitors) = $213M. Development cost: $1.8M. Payback: under 4 days.