Reshaping Financial Industry through Intelligence and Automation

Reshaping Financial Industry through Intelligence and Automation

By Siamak Amirghodsi, VP, Data Management & Analytics, OCC

Siamak Amirghodsi, VP, Data Management & Analytics, OCC

As the world’s largest equity derivatives clearing house, OCC delivers cost-efficient and world-class risk management, clearing and settlement services to the listed options market. Regulated counterparties (CCPs) such as OCC have historically performed well during times of market stress, which has led global policy makers to mandate that more financial transactions be centrally cleared through CCPs following the 2008 financial crisis. The IT model today at OCC reflects the challenging operating environment of the post-2008 financial crisis, which has participants doing more with less, in a shorter delivery cycle. In the banking and financial sectors, resiliency and regulatory-inspired multi-jurisdiction applications, in conjunction with cybersecurity, have become prioritized, resulting in lower capital flow to refresh legacy systems. However, big data, analytics and blockchain are now board-level conversations, which translate into an asymmetric net positive flow of capital to IT teams that can be allocated to platforms, trends and fine-grained services rather than applications.

Data: A Shield, A Sword

The primary function of analytics is to aggregate vast amounts of data to provide dashboards, alerts, anomaly detection, trends and insights, which result in a business narrative that supports high-level decision-making. Quantitative analytics has always been a part of banking/ financial services DNA, but the emphasis today is on moving away from backward-looking KPI/KRI to predictive and prescriptive analytics that can leverage a full view of the enterprise, applying data at the core.

"A successful leader must understand the underpinnings of new technology trends and be willing to take a hands-on approach"

Analytics is the journey of raw data transforming to bi-temporal smart data (structured, unstructured and social) which unites operational, risk and business systems through prescriptive insights to increase ROI. A well-managed analytics track with proper representation at the C-level (Chief Data/Analytic Officer) allows a firm to improve its financial performance depending on how the organization is looking to use data and analytics, as a shield or a sword, in their competitive strategy.

The Challenge

The number one obstacle is resistance to change followed by highly inefficient and barely functioning legacy systems competing for funding with new initiatives and innovation.

Due to resistance to change, antiquated systems prevalent in today’s banking and financial IT systems have made organizations vulnerable to the pitfalls associated with a quick changing landscape. This further hinders an otherwise smart institution’s abilities to react seamlessly to changes in customer behavior and generational and technological trends shaping the future at an unprecedented speed.

Recipe for Success

The market demands faster, accurate delivery in a repetitive manner while adhering to regulatory requirements. Our recipe for successful fast delivery has four parts.

First and foremost, at OCC we configure our team with engineers and Subject Matter Experts that have an ‘inherit bias for action’ and are aware of the mission of the business and the technical level, viewing themselves in light of their own cost-benefit dynamics and their impact on the project and the enterprise.

Second, we strive toward a balanced ratio of existing and new employees. Some of the best and most productive members of my current big data and analytics team came from an existing labor pool with valuable domain knowledge and have committed to learning and contributing to our modern technology stack.

Third, we deploy agile (i.e. Scrum) methodologies in a developer-friendly environment without being stuck on the rituals or artifacts of Scrum itself.

Fourth, by being extremely mindful of team dynamics, we look to provide a fast-paced environment enabling the team to self-manage while operating at the self-actualization level of Maslow’s hierarchy.

Facilitating Technology Transition in Banking

A successful leader must understand the underpinnings of new technology trends and be willing to take a hands-on approach. They must embrace new technology trends, domain vocabularies, a multi-generational workforce and encourage experimentation. Additionally, leaders must actively challenge and foster disruptive technology while avoiding the pitfalls of cookie-cutter architecture that remove ownership from teams. A successful leader should exhibit these qualities: self-awareness, self-management, social awareness and relationship management in order to inspire and manage a diverse multi-functional team.

Leaders should be viewed as facilitators that foster ideas, excellence, quality, and teamwork. And they should be able to expand their resources and create above-normal Alpha-generating systems while engaging their user base.

Heavy-duty Computing in Banking

I am personally excited about the emergence of real-time big data analytic platforms, cognitive computing, Apache Spark as the computing engine, data science and blockchain. The combination of these trends, plus mobile merging at the same point in time, will lead to the creation of an application program interface for banking and financial services with the cognitive ability to enable both internal service providers to compose fine-grained personalized offerings based on micro-segmentation which was not possible before.

Machine learning (deep nets), artificial intelligence and cognitive cloud computing as business platforms of tomorrow are poised to grow exponentially in the next decade. The cognitive market size is up 16x in the past four years with a $32 Billion target fueling a $2 trillion market for decision sciences by 2025, as noted by Ginni Rometty, Chairman, President and CEO of IBM in her keynote address at the IBM World of Watson 2016. For institutions to survive, the financial industry will need to go through a massive automation cycle.

Apache Spark is now emerging as the de facto in-memory distributed computing engine that has a general purpose programming paradigm allowing developers to write code in a modern language in order to solve computationally intense distributed applications at a fraction of the cost and without fully understanding the nuances of parallel programming.

Additionally, data science will facilitate automation via machine learning algorithms in the same manner that quants revolutionized Wall Street in the 1980s and 90s, leading to most of the trading volume being attributed to machine algorithmic trading today. Similarly, data scientists are to banking and financial services what quants were to Wall Street.

The blockchain is a cryptographically secured immutable distributed ledger technology that has the potential to improve inter-institutional trade and remove the inherit inefficiencies in a T+3 settlement cycle while reducing or eliminating the reconcile/repair cycle. There are benefits to being the disruptor in this space, and there are both regulatory and financial frictions at both ends of the pipeline when stepping outside of the trusted eco-system for settlement, conversion or banking.

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