Since data underpins virtually every aspect of the financial sector, from client onboarding processes to risk management reporting, financial services organizations are increasingly focused on data management and infrastructure. But while they allocate vast resources to improve data integration and quality, they often remain slaves – rather than masters – of their own data.
Unfortunately, it’s an uphill battle since the quantity of information available is only increasing, and financial services organizations and investment banks face rising regulatory requirements and pressure to gain greater control and visibility into their unstructured data.
Our experience suggests that few investment banks today – large or small – are getting even a fraction of the potential value they could be from their data. In large part, this is because most organizations are overly reliant on manual processes and interventions to collect, process and analyze data.
This is especially true in the area of compliance where actionable information tends to sit in unstructured data and across a myriad of sources and systems not sufficiently integrated. The cost impact of increased manual activities has led most financial services organizations to focus their resources only on the data that offers immediate value.
In addition, few financial services institutions are able to achieve a ‘single view’ of their data across their organization, since data is scattered across internal systems and processes, fractured, stuck in silos and inaccessible to the rest of the organization.
In light of regulators’ increasing appetite for faster, higher quality reporting from financial institutions, the status quo must change.
We believe that organizations can get to the root of the problem by embracing a new approach to data management and control. Rather than tagging and locking away mountains of data into different systems, organizations should instead use big data technology that can ‘crawl’ through masses of both structured and unstructured data right across the organization to process and pull only the information required – regardless of the format.
Ultimately, this should allow organizations to leverage all of their data, no matter where the data resides or originated, and create ‘real-time’ access to the most recent information available. Among the benefits: risk and finance wouldn’t disagree on financial results, as both would now be pulling from the same root data sets at the same time. From a cost perspective, operations wouldn’t need to expand headcount or increase spend to respond to regulatory reporting requirements.
Those organizations that create a more innovative, competitive view of their data can also use predictive analytics in their operations to reduce trading risk, improve customer interactions and uncover new opportunities to grow their business and portfolios.
KPMG’s proprietary data solution leverages big data approaches and KPMG’s unique insight and business acumen to offer companies a clear roadmap to lowering costs while realizing improvements that meet regulatory and compliance challenges, and support operational efficiencies.
This new solution platform allows organizations to combine data aggregation and search, intelligent data extraction, policy automation and efficient workflow processes with a speed, accuracy, completeness and unit price that would not have been possible just a few years ago. For example, when applied to areas such as costly client onboarding processes, organizations can greatly improve the quality of their data and reporting and reduce the costs of ongoing operations, maintenance and infrastructure.
By taking a new approach to data management, these organizations will not only satisfy today’s regulatory demands but they can also rise above the fray to become true data masters.
Addressing the principal transformation issues senior executives struggle with today.
There is a rising need for banks to offset increased capital markets automation by investing in operational risk management.
Investment banks review finance shared service models of offshoring and outsourcing.