Araya Solomon is Managing Director and Global Head for Capital Markets at Slalom with 24+ years of experience in APAC, EMEA and AMS.
In the financial services industry, it’s common to see legacy systems, built on top of slightly newer legacy systems and connected with some innovation, accumulate over time. These layers reflect cumulative decisions packaged in technology. Additionally, regulatory timelines exacerbate this with temporary solutions, increasing operational support demands. In most instances, organizations live with tactical solutions for years before remediating the technology stack born from compliance and regulatory requirements.
These obstacles are prompting wide-ranging transformations in the industry. To better manage the outcomes of these transformation programs, financial services companies need to enhance their ability to deliver value. This optimization will enable them to surpass customers’ expectations, build loyalty and gain a competitive advantage. So, what’s the path forward?
1. Simplify your ecosystem.
For those leveraging software packages, simplifying to the lowest number of solutions should be a priority.
Best-of-breed solutions are reserved for tier 1 institutions that can absorb the cost with high transaction volumes or numerous clients. Smaller organizations (i.e., those outside tier 2) that attempt predominantly custom and bespoke solutions often mitigate ongoing costs through outsourcing change and run services. However, those with an immature operating model and sourcing strategy might see slower-than-optimal delivery of solutions. Some have attempted “productizing” their solutions and failed to launch utility to market, realizing the significant difference between running their own technology and servicing many clients.
Therefore, smaller financial service institutions are leaving the technology arms race to tier 1 financial services clients and are moving instead to off-the-shelf software packages.
2. Streamline your environment for build and change.
Incorporating DevOps, continuous integration/continuous development (CI/CD), automation in testing and APIs for integration is essential for managing change swiftly with controls and quality assurance in place. These methods can reduce change costs. Although not a novel idea, I’ve found that the holistic adoption of these practices in financial service environments is still limited.
To fully benefit, banks need a dedicated program for CI/CD automation. While mainframe solutions pose key constraints to these methods, the principle remains the same: delivering within a framework and pattern that ensures rapid delivery, validation and expected outcomes.
3. Modernize your mainframe.
Modernizing mainframes is a must for transitioning to modern trading solutions. While mainframes provide stability, they lack flexibility, which hinders innovation. Their batch processing cycles are incompatible with real-time information demands; this causes delays in data availability.
Financial services firms must adopt flexible, scalable and real-time processing systems to improve transaction handling and reporting. Additionally, as talent becomes scarcer, relying on outdated platforms is increasingly untenable, making modernization a critical priority.
4. Prioritize the cloud.
Initially, cloud adoption focused on cost and scalability, but the real business case lies in access to innovation. Cloud migration can reduce costs while also enabling organizations to leverage newer solutions for ongoing needs (e.g., supporting new trading strategies) and regulatory requirements (e.g., Basel IV). Solutions hosted in financial services companies’ data centers simply do not benefit from the elastic computing demands necessary in today’s environment, where information is a key source of growth.
Cloud computing’s pay-per-usage basis is more attractive than data centers’ capital-intensive infrastructure. Financial service institutions that adopt cloud infrastructure often experience higher consumption rates than expected. On the positive side, for example, risk managers can run an increased number of value-at-risk scenarios by leveraging the elastic compute environment, sometimes doubling the number of scenarios to better manage exposure and risk.
On the negative side, organizations have not effectively managed financial operations as they relate to cloud usage. Although the business case for cloud computing is clear, firms need to address the challenge of managing costs and rebilling to the business based on consumption and share of costs.
Ultimately, cloud infrastructure is a gateway to fully leveraging modern technology and solutions such as advanced analytics to better understand the value/risk of an asset, using multiple streams of information from structured (e.g., trading and transaction) and unstructured data (e.g., news, web analytics, email, SharePoint, etc.).
5. Govern your data.
Financial institutions that effectively govern and master their data will lead in leveraging newer technologies. A significant challenge for clients in financial services is the ongoing need for reconciliation, which increases operational costs. Adopting a common data model, such as ISO 20022, FpML or CDM based on ISDA working group directions, provides a cost-effective path forward. While ISDA emphasizes the broader potential of CDM for the future of financial services, it acknowledges that these models play crucial roles in improving the efficiency, accuracy and reliability of financial market operations.
Other means of governing data include using a platform that covers comprehensive capability front-to-back. Solutions like Nasdaq Calypso and Murex in trading and risk provide a central view of data across the trade lifecycle. (Full disclosure: Nasdaq is a Slalom client, and I or my company works directly or indirectly with the vendors referenced in this section.) By applying a common data model across the entire trade life cycle, firms can mitigate the need to continuously reconcile across capabilities that reside on multiple technology stacks. For firms that have struggled with disparate systems, adopting common solutions can reduce overall organizational effort and associated operational risk.
Newer vendor solutions in the market have mastered and provisioned a solution that effectively utilizes a bitemporal data model to support accuracy in capturing and reporting, allowing firms to aggregate and disaggregate data in real time. While some firms have opted to externalize their data to enrich it with other sources due to reporting constraints, this approach enables the retention of data in one common source.
Cloud data platforms such as Databricks and Snowflake can accelerate this process by serving as data warehouses and analytics platforms, aggregating various sources of data in a bespoke environment. If these platforms adopt vendor data models such as Nasdaq Calypso, the transformation could be profound.
In summary, to better manage outcomes and deliver transformation program value, banks must simplify their ecosystems, streamline environments for change, modernize mainframes, prioritize cloud adoption and effectively manage their data.
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