How Automation and Data Quality Are Boosting Operational Risk Manageme…

How Automation and Data Quality Are Boosting Operational Risk Manageme…




Since the financial crash of 2008, commercial banking has experienced a monumental shift in regulatory reform to both manage and mitigate against operational risk. This transformation has been combined with an increased focus on improving enterprise-wide efficiency, profitability and shareholder value.

Established regulations such as Dodd-Frank, Sarbanes-Oxley (SOX), Basel II (superseded by Basel III) and the forthcoming rollout of MiFID II, seek to aggregate and enhance how edges protect themselves against threats to governance, risk and compliance (GRC).

Data that is currently held is already being interrogated using machine learning, bots, virtual assistants and artificial intelligence (AI). This data has immense strength to be harnessed for efficiency and this trend will continue to evolve in the years to come. A number of the world’s top commercial edges are investing considerably in this area and it is expected that those who continue on this automation journey will gain a technological competitive advantage.

Recent examples of this include JPMorgan’s program, called COIN (Contract Intelligence), which does the repetitive job of interpreting commercial-loan agreements – this course of action before consumed 360,000 hours of lawyers’ time, yearly. The software reviews documents in seconds, is less error-inclined and never takes time off for holidays or rest – all making sound business sense and helping to reduce cost and increase profits.

CaixaBank is also maximising the use of IBM’s Watson to streamline processes. Pere Nebot, CIO, sees this investment as being valuable: “Connective computing is the new trend in commercial banking technology and in my opinion this will change interactions between customers and the bank and make life easier. Our connective architecture with Watson will allow us to work more smartly and give better service to our customers.” The output of AI systems like Watson, with the assistance of document automation software, has the ability to create and deliver a seamless course of action for the accurate generation of business-basic lending documents.

Many of the world’s top edges have grown exponentially over the past few decades – by global expansions, acquisitions and mergers – and processes that supervise governance have become slightly uncoordinated and inefficient. This view is supported by a PwC report which states that, “While a number of edges have begun the commercial lending transformation course of action, some have not had the focus on data strategy that is needed to meet emerging regulatory reporting requirements cost efficiently… an inefficient commercial lending loan origination capability and related data ecosystem will put a bank at a competitive disadvantage.”

Commercial edges are operating in a data-pushed world, which in turn leaves data accuracy as an area of possible exposure and a ineffective link in the first line of defence in risk management. Automation of processes in data and documentation output offers a smooth route for companies to save money, increase accuracy and streamline processes, consequently reducing risk. According to the British Banking Association: Operational risk in market-related activities can arise from many supplies, such as poor or inefficient data management, systems and processes.”

The real value in “Big Data” lies in how to analyse and output specific customer data to get better outcomes. This acts as a keystone in risk management and has the strength to change the “garbage in, garbage out” viewpoint to “quality in, quality out” with a standardised and clean output format.

In turn, this aids compliance with Basel II and SOX, in terms of execution and reducing data entry errors by having better delivery and business course of action management. It is of utmost importance that the validity of information and the quality of data is not compromised during processing and output – as the financial and reputational repercussions here are huge.

Some of the world’s most renowned banking leaders have echoed the view that innovation in software and new technology has the strength to make commercial banking more proficient. Ralph Hamers, CEO at ING, states that: “if you are the first mover and to disrupt, you will lose some income on one side, but you will be able to grow more aggressively. The changes we have made have allowed us to course of action quicker responses to credit requests, which improves the service we give to customers.”

A number of challenger edges (such as the likes of Metro Bank and Aldermore) are continuing to disrupt the banking ecosystem by gaining more market proportion, which is keeping larger companies on their toes and driving innovation and efficiencies across the banking sector.

Improving business processes with document automation has the strength to propel already the largest and most established commercial edges into a position of strategic, competitive advantage. This focus on document quality as a cornerstone of GRC, particularly in such a data-high industry, should help to offset at the minimum some of the scrutiny of the past decade.




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