A recent study by Mosaic Smart Data, a leading capital markets data analytics firm, has shed light on significant challenges faced by investment banks in managing data quality and integrity.
Despite substantial annual investments in data management solutions, reaching approximately USD 88 million, many banks are still struggling to leverage AI effectively in their front office operations.
Two-thirds of banks grapple with the most critical data
The research, encompassing a detailed analysis of six million FICC (Fixed Income, Currencies, and Commodities) transactions across global and regional investment banks, reveals a concerning scenario:
Data Quality and Integrity: 66% of the banks surveyed are grappling with issues related to data quality, including incomplete data capture and significant gaps in important data points.
Lack of Real-Time Analytics: A staggering 83% of banks do not have real-time access to transaction data or analytics capabilities, primarily due to the absence of centralized data repositories.
Access to Data: Two-thirds of the banks report difficulties in accessing the most critical data for their analytics, citing reasons such as data fragmentation or total inaccessibility.
Unfit Reference Data: Half of the banks face challenges with reference data, lacking a unified counterparty identifier, which is crucial for client static data.
“No powerful analytics or AI can fully function if data is not normalized”
Matthew Hodgson, CEO and founder of Mosaic Smart Data, said: “As AI continues to evolve, so does the sophistication of the data analytics solutions it underpins – but before banks can take advantage of this they must first address the ‘state’ of their data.
“No powerful analytics or AI can fully function if data is not normalized, maintained in an orderly fashion and gaps in the data enriched. This must be addressed before an analytics programme can progress successfully. It is now a crucial time to invest in data and analytics to revolutionize the front office of investment banks – but ROI must be guaranteed in the current economic climate, and this begins with better data health. Making the right changes today can deliver significant long-term returns.”
Key challenges faced by investment banks
The study by Mosaic Smart Data highlights several critical challenges faced by investment banks in the front office, which hinder the effectiveness of data analytics and the realization of potential returns on investment. These challenges include data fragmentation, difficulties in handling voice data, gaps in instrument static data, and issues with counterparty static data.
1. Data Fragmentation:
Data fragmentation poses a significant challenge as it leads to inconsistencies and inefficiencies. Key aspects of this challenge include:
Multiple Data Sources: Banks often need to integrate 5-10 different data sources to gain a comprehensive view, leading to complex data management and increased chances of discrepancies.
Disparate Front-End Systems: Front office staff typically use 2-3 systems for their data needs. These systems often have multiple pages and interfaces, adding to the complexity and time required to access and interpret data.
2. Voice Data Collection:
Voice data, representing a considerable portion of transactions, especially in derivatives and unstructured communications, presents two main challenges:
Missed Voice Trades: Many institutions fail to capture missed voice inquiry data due to inadequate implementation of sales-to-trader ticketing systems. This gap results in a loss of valuable data that could inform trading decisions and client relations.
Bloomberg Chats: Although Bloomberg chats contain rich trade and inquiry data, the unstructured nature of this data makes it challenging to extract actionable insights. Efficient technologies for processing and analyzing this unstructured data are scarce and often not effectively implemented.
3. Instrument Static Data Gaps:
Instrument static data, particularly for Interest Rate Swaps and Credit Redeemable Bonds, has significant gaps that hinder risk assessment and analytics:
Interest Rate Swaps (IRS): Critical data points such as Delta/DV01, floating rate index, and interest rate payment conventions are often missing. This lack of data prevents comprehensive risk analysis and understanding of IRS flows.
Credit Redeemable Bonds: Despite the increasing prevalence of these bonds, data on redemption dates, prices, and yield to worst are often incomplete or missing. This gap significantly impacts the risk profiling and potential revenue generation from these instruments.
4. Counterparty Static Data Issues:
Counterparty static data is crucial for risk analysis, client relationship management, and regulatory compliance. The challenges in this area include:
Inconsistent Identifier Mapping: A lack of consistent unique identifiers across systems makes it challenging to track and analyze counterparty interactions and exposures accurately.
Fragmented Counterparty Data: Counterparty data is often scattered across various systems, such as CRMs, trading, and pricing systems, making it difficult to obtain a unified view of counterparty relationships.
Poor Granularity and Mis-categorization: The lack of detailed segmentation and categorization of counterparties, coupled with frequent mis-categorizations, hampers effective risk analysis and client segmentation strategies.
