The effects of flawed financial products at the heart of the credit crunch are still apparent in banking today, with financial institutions navigating volatile yet cautious markets, and regulators casting a watchful and ever-present eye. Since 2008, the capital markets may have re-invented themselves, but MiFID II, Basel III, FRTB and Dodd Frank regulation present banks with a very real challenge to profitability and agility. It’s up to banks to create a sustainable compliance environment and keep pace with regulatory demands while keeping innovation as top of the technology agenda, and this is where analytics is proving a real game-changer. But how are banks achieving high-impact business outcomes in today’s regulatory landscape, and how can operationalized analytics help?
While successful analytics focused companies can make the useful application of advanced analytics look easy, the truth of the matter is that the practical and profitable application of analytics is a complex, multistep process only a handful of companies have successfully deployed at scale. By ‘operationalizing’ analytics using the right tools, processes, best practice frameworks and accelerators, businesses, particularly banks, have been able to productionize analytical solutions. The result in many cases are consolidated analytical platforms that deliver automated results that are making a real difference in today’s strict regulatory landscape.
of data across all regulatory projects is the same, yet in banks, most regulations have separate data integration projects; institutionalizing redundancy and repetition. What this means is that banks may have a separate in-house or outsourced team for each regulatory framework they need to comply with - despite much of the data required for compliance being common across different regulatory frameworks.
It’s obviously an ineffective way to work, and it’s also not cost effective by any means, which is why banks are looking to analytics to help through simplification and automation. By introducing a single platform for finance and regulatory reporting, complete with end-to-end data lineage and governance, banks may be able to improve data quality and transparency and end siloed work to ensure compliance across several regulatory frameworks. What does that mean? For the large part, it means banks are getting automation right, and that they are using it to save a lot of time and money when it comes to being compliant.
is a very good example of analytics in action to keep pace with regulatory demands. After the financial crisis of 2008, the bank felt increased pressure for reporting to meet compliance requirements, but distributed, siloed data was creating reporting challenges. UniCredit focused on creating a common global data platform with consistent data definitions. The data platform has improved data governance and eliminated redundant data to help achieve regulatory compliance.
