When it comes to risk management and technology, the game is changing for back office (BO) operations. The days when processing professionals used to care mainly about confirmations, settlements, payments, matching, reconciliations, disputes, postings, and so on, are past. They need to learn how to manage risk by using tools built for front office traders and risk managers, and learn how to interpret back office data for better process optimisation.
Three major trends can be identified in back office processes in developed economies:
Typical Activities for Post-trade Industrialisation
These processes will be standardised and delegated to smart microservices. Services in a microservice architecture (MSA) are often processes that communicate with each other in order to fulfill a specific goal using technology-agnostic protocols. This allows rapid and simplified deployment on multiple platforms with a minimum of human interventions.
Emerging technologies (such as the Cloud, microservices, Machine Learning) combined with the standardisation of target operating models (TOMs are processes used in the same way by different banks, since there is no need to reinvent the wheel for each bank) will automate these functions, making them more efficient, cheaper to maintain – as well as increasing straight through processing (STP means less exception handling, less manual interventions).
And this is happening very fast. It means there will be huge pressure on the workforce in these areas that stand to be replaced by ‘smart solutions’. However, emerging countries’ back office manual interventions still have years to go before being industrialised.
The new game in the back office world is risk management. It is best to illustrate this through a few examples:
Collteral management (collateral agreements management, interest management, margin management, allocation management, dispute management and settlement management) used to be a back office function until the 2008 crisis. Then there started to be a remarkable shift. The need for collateralised trades exploded, and collateral management moved from the back office corner to the heart of managing funding optimisation and liquidity risk.
The collateral manager, among other things, needs to run ‘what-if’ scenarios on market conditions [simulating market data movements and changes in companies’ behaviour] in order to identify new patterns of margin requirements, and also to run complex optimisation scenarios to select the cheapest-to-deliver collateral or receive-the-best-quality collateral. These are totally new skills and technological needs that are becoming very desirable. This shift has mostly been seen in Europe.
Payment Management (Intra-Day Liquidity)
Where it was once enough to issue the correct payments on time, new skills are required from the payment officer. In addition to his standard operations, he needs to run what-if scenarios in order to identify patterns under stress regarding what margins to be called, what payments to be made (and at what time in the day), identifying the required liquidity venues under each scenario, and building daily corresponding risk profiles in order to be prepared for any crisis.
This requires an understanding of how market data movements and changes in businesses’ circumstances impact different margin pricings and products – and of course, tight coordination with the collateral management desk in order to optimise liquidity venues. This is a regulatory compliance (according to Basel III) for intra-day liquidity.
IFRS 9 is a complete set of accounting rules imposed to replace IFRS 39 (which focuses on hedge accounting) in the back office to better integrate risk management in accounting. Accountants need to be agile thinkers, demonstrate a new array of skills and knowledge in credit cisk, and to better coordinate with credit risk experts in order to successfully implement IFRS 9. For example, they need to estimate the expected loss of a position during its entire life and account for it regularly. IFRS 9 compliance should be ready for banks by January 1st 2018.
Back office operations are assigned costs per transaction. In addition to the standard cost of operations, key performance indicators (KPIs) such as the percentage of incorrect transaction entries, percentage of missing payment information, and so on, will have additional costs linked to them and this cost will be reflected in the desk profit and loss reports.There will be a need to run what-if scenarios in markets movements to simulate late payments, defaults, wrong data, and so on, and identify the worst costs to see the impact. This is a new concept that back office people were not previously concerned with.
These are few examples where Risk Management and Simulation are becoming part of the Back-Office landscape.
Back Office Processing Automation
KPIs from operations are a good input for Machine Learning algorithms to optimise back office workflows and adapting rules for better STP. ML has real potential to reduce workload in client onboarding, loans processing, payments and settlements workflows, as well as reconciliations.
Applying this technology to these areas will dramatically enhance productivity and reduce costs. It also means it will impact the back office workforce and the corresponding people need to be deployed in different areas. JPMorgan Chase’s Machine Learning program, COIN, is parsing financial deals that once kept legal teams busy for thousands of hours. The software reviews documents in seconds, is less error-prone and never asks for a holiday.
This is an ideal time for banks’ back office executive management to think about how to be creative and reinvent the profession in order to benefit from existing resources and technologies.