Science fiction has painted a vivid, if slightly dystopian picture of what a future with intelligent robots might look like. Their triumph on humans may very well seem to be a distant reality but as technology progresses, there is no denying that the advent of robots with near-human levels of cognizance, is not too far.
Robots with advanced artificial intelligence (AI) functionalities could have an unprecedented effect on the word’s economy. In the car insurance industry, bots with cognitive capabilities are already scanning claims and analyzing customer-proofs of vehicle damages to deduce the right payout for each case. With these super-workers that are programmed with human insight and enviable computing speed, companies no longer need to send assessors onsite to validate claims.
The financial and trade market has been quick to leverage these developments. Traders and asset servicers are driving efficiency, curbing risk, and enhancing the quality of service through automation enabled by advanced robotics and artificial intelligence (AI).
Empowering Next-Gen Derivatives Processing with Smart ‘Bots’
The global economic crisis and the ensuing regulatory reforms compelled the financial industry to change the way over the counter (OTC) and exchange-based derivatives are processed and traded. Increased market volatility and an understandably heightened focus on risk management motivated traders to seek solutions that could address post trade processing inefficiencies and associated operational risks.
Today, the global derivatives processing market is back on an aggressive growth trajectory, showing no signs of slowing down. Financial institutions have several opportunities to enhance revenue, rope in new clients, and expand their footprint. But are they able to fully capitalize on them?
A steady increase in traded volumes, coupled with the complex nature of derivatives have led to protracted confirmation cycles with stringent deadlines. The derivatives transaction process demands 100 percent accuracy and timeliness, thus manual intervention doesn’t always serve the purpose. Manual processing poses a series of challenges since the chances of human errors remain high.
Additionally, manual processing also results in confirmation arrears and difficulties for the front office to allocate capital and assets to financial market instruments. These set of difficulties may very well be attributed to the nature of the OTC derivatives market where difference in processes adopted by multiple processing venues (countries as per their), quite inevitably disrupts an established processing model. For instance, derivatives contracts often come in different formats, from different regions and with diverse language inputs across multiple input channels. To maintain uniformity, a processing venue is often forced to review process design and change current practices, which evidently reduces efficiency and creates challenges for regulatory compliances. Apart from this, venue-based discrepancies also arise from conflicting approaches, including whether a transaction will be executed by voice or electronically, through a central-limit-order-book (CLOB) or request-for-quote (RFQ).
These challenges highlight the need to integrate robotic process automation (RPA), AI and Natural Language Processing within derivatives processing. RPA helps in governing the entire trading process and implementing the same validations and steps as a human working on the system would do, but with greater accuracy and expediency. It also significantly shortens the long phases of the overall trading cycle, eventually curbing the chances of risks and streamlining processes that are repetitive and rule-based.
Intelligent RPA, on the other hand, leverages the cognitive capabilities of artificial intelligence (AI) to address even the minutest of inefficiencies within current processes that often go unnoticed. It facilitates the adoption of more targeted solutions such as cognitive robotic platforms that combine analytics with RPA and highly specialized systems, which simplify data processing.
Shifting Gears – Infusing Cognitive Technology
Clearing houses and derivative exchanges are investing in robotic process automation (RPA) and associated technological innovations to streamline crucial points of the trading process such as reconciliations and cash forecasting. This has accelerated operational activities, despite mounting trade volumes and quotes.
Advancements like algorithmic trading, which accounts for the majority of derivative exchanges today, is an example of innovative derivative trading styles used by market participants. Other successful models include the use of robotic process automation (RPA) in combination with technologies like natural language processing (NLP) and optical character recognition (OCR), which enable computers to automatically apprehend and process data while initiating automated workflows. These associated technologies help automate lateral processing tasks that used to be performed by humans.
The next level of derivatives trading process automation, may well be attributed to the integration of artificial intelligence (AI) within the existing RPA framework. This can open up new possibilities in crucial areas like developing intelligence with Natural Language Processing (NLP) and natural language generation (NLG) and integrating it across financial operations, business strategies and regulations. Cognitive technologies like these simplify highly perceptual and judgment-based tasks through its potential to discern images, recognize handwriting and leverage Natural Language Processing (NLP) to interpret complex information.
Larger trade volumes and increased automation can only be built on scalable, robust processing infrastructure that does not compromise on run-time performance. Trading enterprises with an eye on the future, need to be open to automating even small tasks, in order to reduce operational risks and costs across the entire derivatives trade processing lifecycle.
Today, clearing houses and derivative exchanges that enjoy high levels of efficiency and productivity, do so because of continuous and early IT investments. A strategic marriage with the right technology has made the difference between success and irrelevance in an ultra-competitive landscape.