Until a few years ago, the system architecture diagrams of most financial services firms would have shown a single system (possibly only a database) proudly labelled ‘CDB’; a widely adopted acronym for ‘Client Database’. Surrounding this often ignored orphan data-set existed a loose arrangement of immature business management processes and sub-standard (possibly even non-existent) data management practices.
The global financial crisis and an ensuing wave of regulations changed that; in recent years firms have steadily embraced the need to address these legacy shortcomings with strategic investment in client (legal entity) data management capabilities. In the aftermath of the 2008 crisis there was an exponential increase in financial sector regulation. Financial services firms made increasing use of technology to comply with each new regulation; often by bolting separate solutions onto their existing client data architecture for each gap in their data or process.
The introduction of Regtech
This technology was generally an adaption of otherwise available industry non-specific data management and workflow applications. High project costs and long delivery times typified these implementations; they were implemented piecemeal, with elongated operational processes and costs, and obscured the ability of a firm to achieve a holistic view of its regulatory compliance. This brought about evolution, in that there was a need for specialist technology providers to the financial industry (fintech), to create capability fit for the handling of the ever-evolving regulatory requirements governing the financial industry. Regulatory technology (regtech) was born.
Regtech has sparked the progression in technology solutions for client data; from pure-play data storage/management capability, to the development and adoption of new purpose-built platforms that reduce time to market and enable the leveraging of industry-specific functionality, business domain knowledge and regulatory rule sets. In the client data domain, regtech can be defined as technology platforms designed to facilitate the delivery of auditable regulatory compliance, by enabling the management of client data across many intricate and interdependent ‘client lifecycle management’ (CLM) business processes which encompass multiple products and jurisdictions on a single extensible platform.
In the broad context of this regtech definition outlined above, an enterprise CLM platform should specifically be a data management platform with flexible workflow capabilities that, combined with a rules engine, is able to deliver robust regulatory compliance whilst also delivering active operational processes and data management. This is a complex space where client onboarding, account opening and client data management activities should be streamlined across product lines, business silos and geographical jurisdictions; with automated enforcement of compliance across client due diligence processes (KYC/AML) and industry tax and trading regulations (Dodd Frank, EMIR, FATCA and CRS).
Improving the process
The predicament of fit-for-purpose client data has always besieged financial services institutions, but it has been largely ignored, worked around or part-solved with the ‘Client Database (CDB)’. However, in this age of regulation, firms have had to understand how important access to high-quality, audited client data is to the success of their business and regulatory operations. With a renewed focus on client data, firms realise that client onboarding processes are fundamentally the processes through which data about new clients is introduced into the enterprise, for the first time.
It is therefore clear that sophisticated data orchestration processes are required across product, credit, legal and compliance operations, rather than the complex and varied manual processes previously used with legacy systems. Without fully understanding the importance of data, many onboarding technology developments undertaken in recent years have focussed on more obvious facets of onboarding; namely business process flows, functional capabilities, screen design, and user experience. While these aspects are important to the holistic performance of CLM deployments, they have, in our opinion, overlooked the need to manage client data strategically; an unfortunate outcome that has either overwhelmed or significantly hindered many strategic platform implementations.
The key issue here is the ability to master client data in a way that can adapt to future changes. Systems managing this level of complexity require a single shared data model spanning the database model itself, the code and relevant interfaces. The data model should be flexible; allowing for entity types, reference data types and attributes to be added via configuration files.
A centralised Data Management Engine is required to apply workflow processes and invoke the rules engine to apply business rules to entity data, regardless of the data source (inbound internal message, external data provider, or manual data input etc). While proper data management greatly assists and improves the onboarding process itself (with reduction in elapsed time end-to-end), it is often in post-onboarding activities like client servicing, client engagement and, specific to regtech, compliance and regulatory reporting, that the significant advantages of data management enhancement in the onboarding process is really evident.
Financial institutions have a mass of client and counterparty data that needs to be collected, consumed, aggregated and utilised; both for the conduct of business and for the purposes of remaining compliant with the many regulations that now govern their activities. Serving this requirement is an ever-evolving ecosystem of data vendors, service providers and content utilities. In order to effectively utilise the offerings of the various service providers, a rules-driven data connectivity tool is imperative.
The only sustainable solution is for firms to deploy a data interface capability that is able to manage the required connectivity to multiple sources. Not only must the interface manage the connectivity, but it must also detect and remediate data discrepancies by applying data completeness and quality rules in real-time. The cost, time and risk overhead of doing this manually is now untenable; meaning that automation is both urgent and vital. Given the above, no properly managed firm should need to continually reshape their existing solution-set as new regulations come into play, or as new compliance and data services come onto the market.
The ability to onboard customers quickly and accurately, and connect to emergent utilities for best customer experience and regulatory compliance is equally critical. Best-of-breed regtech in the CLM space should offer integrated onboarding workflow with master-data-management capabilities. Thus allowing financial services companies to execute efficient and regulation aligned onboarding, well-organised and accurate compliance refreshes of their customer base (rolling due diligence), whilst also offering the foundation for significant business benefits to be gained from up-sell and cross-sell opportunities within a global firm.
This approach will also deliver significant benefits in terms of reducing business cost and time to market of these initiatives, and perhaps more importantly give the financial firm the agility to respond to the next wave of regulation that will surely come.
Posted in Bobsguide Ben Marsh – CEO, iMeta