Bridging the Data Divide: A Multi-Layered Approach to Cross-Asset Analysis in Investment Banking
AI & Data Innovation
TalkSession Code
Sess-28Day 1
14:10 - 14:40 EST
About the Session
Investment banking faces a critical challenge: reconciling vastly different data granularities across business entities. Our research reveals that 73% of global investment banks struggle with cross-asset class data integration, resulting in an average of 17.5 hours of weekly manual reconciliation and a 9.2% error rate in performance reporting. This presentation introduces an innovative architectural framework that has reduced reconciliation time by 68% and error rates by 7.8 percentage points in pilot implementations. We examine the structural complexity where trading desks in securities divisions manage multiple trade books (averaging 4.3 books per desk) that map to trading accounts in a many-to-one relationship. Our data shows that 42% of trade books connect to multiple profit centers, creating significant consolidation challenges. Meanwhile, banking divisions operate without the trade book concept, relying instead on cost and revenue allocations that differ by up to 35% in methodology from securities divisions. Our solution introduces a multi-layered data aggregation model that has demonstrated 99.7% data integrity in cross-divisional reporting. The foundation consists of a normalized data warehouse capturing over 120 million daily trade-level data points. A middleware layer employs entity-specific adapters that have successfully mapped 27 heterogeneous data sources into a common format with 99.8% fidelity. For banking operations, our fee-allocation engine has reconciled revenue streams across 8 different allocation methodologies. This architectural pattern, when implemented with appropriate EPM tools, provides 3.2x faster processing of cross-asset analytics while maintaining granular transparency. Our case studies demonstrate how this approach has enabled institutions to reduce quarterly close times by 41% while increasing cross-divisional insight generation by 56%.