Nikhil Aggarwal is the Global Head of Surveillance Parameter Optimisation and Tuning, Group Financial Crime Compliance (FCC) at Standard Chartered Bank. He leads a newly created global team of 35 data scientists tasked with deploying advanced analytics solutions to set and continuously maintain the operating parameters of FCC monitoring and screening systems. Nikhil has extensively used data-mining, statistical modeling, segmentation and financial modeling to formulate, drive, and embed risk management strategy. Prior to joining SCB, Nikhil held analytics positions with Bank of America, Citigroup and Capital One.
We sat down with him ahead of his presentation at the Big Data & Analytics for Banking Summit, taking place in New York this December 7-8.
How has your role evolved over the past 12-18 months?
I transitioned from a regional to global analytics leadership role in 2015. As the Head of the Surveillance Parameter Optimization and Tuning (SPOT) team, I lead a team of data scientists and statisticians who are tasked with delivering advanced analytics solutions to set and continuously maintain the operating parameters of the Bank’s financial crime compliance systems.
These systems encompass transaction monitoring, transaction screening and name screening. My team’s core responsibility is to improve the effectiveness and efficiency of transaction monitoring by leveraging various analytics methodologies and tools.
The principal delivery on transaction screening and name screening streams is to increase the accuracy in screening models by deploying matching rules, algorithms, and fuzzy logic to optimize sensitivity settings. Being a global team, SPOT is able to apply consistent analytics methodologies across the Bank’s footprint, and build out adaptive yet scalable analytics solutions.
What advice would you give someone wanting to become a Chief Analytics Officer, and what are the core skills one needs to thrive within the role?
Given the increasing importance and recognition of analytics leadership roles, individuals aspiring to be a Chief Analytics Officer (CAO) must recognize the 'ask' and develop a sustainable strategy and execution plan. I split my time between four equally important focal areas that I believe apply to individuals in different analytical verticals.
First, Delivery – how have we provided analytical solutions and insights to solve the most pressing business challenges? Can we quantify the analytics value add? Second, Talent Management - have I developed the right target operating model, have I hired the right mix of individuals and have I structured them optimally? How am I developing my talent? Third, Evangelism – A CAO must continuously ‘sell’, influence and embed the analytics brand. Building partnerships with key stakeholders and celebrating both ‘small wins’ and ‘big bangs’ are key. Fourth, Analytics Innovation - As a data scientist, my responsibility is to mine the data to find unusual financial activity. The core challenge is to translate qualitative hypotheses into robust predictive models.
In terms of skills, a healthy mix of baseline skills (analytical curiosity, programming, statistics, domain knowledge, and understanding of platforms) coupled with leadership savvy (crafting strategy, leading on execution, 'evangelizing', building partnerships, managing and mentoring a team) would well suit a CAO. A tough ask indeed. A successful CAO knows which situation requires the right ‘response proportions.’
Data is exploding, the number of analysts is flattening and expectations and demand is growing – how does one best manage in this scenario? Should the focus be on processes or business problems?
It is especially important for analytics teams to focus on quantifying the value-add of their work and simultaneously focus on processes and business problems. Analytics Teams cannot see these as mutually exclusive areas.
As we further the big data journey, teams need to constantly question the existing data models and look for opportunities to experiment with newer types of data being collected and build out comprehensive data structures. ‘New’ analytics techniques employing cognitive computing, deep learning and natural language processing need to be explored further.
A deep understanding of both revenue and cost drivers at a granular level and the linking of analytical recommendations to these drivers go a long way in explaining the contributions of different analytical techniques and models to a broader business audience. Increasingly, traceability is an important theme.
Analytics teams must place emphasis on documenting procedures, and providing non-technical commentary on their work so that these can be easily understood by governance teams in particular.
What is the biggest challenge you face in your role today and how are you looking to tackle it?
Talent identification and retention is a key challenge. We need to identify aspiring data scientists who are curious about the financial crime risk domain but also able to build strong partnerships with the broader team. Given the focus in this vertical, many organizations are building up their teams. This makes talent retention a key priority as well. Candidates with a specific expertise in financial crime risk analytics are in high demand, and we have expanded our conversation with individuals to include those who have an analytics background across different verticals including marketing analytics and credit risk analytics.
Once a candidate is on board, comprehensive training is provided in both the domain and specific applications of analytics to help them identify, manage and mitigate financial crime risk. Development opportunities including accelerated leadership positions are presented to high-potential team members to support talent retention.
What is the biggest challenge faced by the analytics/big data industry currently and in what ways does this affect your business?
A number of technology companies have been designing new and innovative solutions to meet the increasing demand of banks investing in financial crime and compliance analytics. While this is incredibly exciting, these solutions need to be explored further in how analytics modules impact downstream operational workflows.
Technology companies should not simply state that their solutions leverage text mining, neural networks or natural language processing. They should instead suggest solutions that are flexible and robust to meet ever-growing needs. Repeatability and scalability are top of mind attributes.
Where do you see being the biggest area of investment in analytics within your industry over the next 12 months?
There is an increasing focus across two broad categories: i) people and ii) technology solutions within financial crime risk analytics. Banks are increasingly recognizing the importance of creating a dedicated financial crime risk analytics team and are investing in talent at all levels – ranging from analysts to senior leadership roles.
In terms of analytics, the focus is on technologies that apply ‘smarter’ and “more powerful” algorithms in risk identification, and tools that improve the efficiency and effectiveness of operational workflows. One example of analytics innovation is the optimization of alert generation, and the subsequent routing of these alerts to specialized investigations teams.
Alerts are generated due to unusual and potentially suspicious transaction behavior and represent the first human touch point in a generic assessment of anti-money laundering risk. With advances in analytics, sophisticated algorithms combine unusual transaction behavior with the full history of client transactions and other behavioral patterns to form an enriched and composite view of aggregate risk. The algorithms then rank the overall riskiness of ‘events’, and channel these events to the workflow queues of specialized investigations teams.
What is the biggest future trend you see within the Big Data and Analytics space? For example, what opportunities do new data sources such as IoT, web, social, mobile, and eCommerce present?
The biggest unexploited opportunity in Banking analytics is the lack of ‘connectivity’ within the enterprise. For example, most financial institutions have built out separate analytics practices including marketing and digital (web, social, and mobile) analytics, credit risk analytics, operations analytics, fraud analytics and compliance analytics.
These ‘siloed’ teams often leverage the same underlying data structures as they mine the data to uncover both incremental revenue opportunities and risk ‘hotspots.’ A marketing analytics team looks through the ‘lens’ of increasing activation and product penetration by cross-selling across channels. As part of this analysis, they could potentially uncover fraudulent behavior and potential anomalies in usage that may present evidence of money laundering.
Organizations need to change this myopic mindset by encouraging the formation of cross-banking analytics projects teams. This therefore enables the potential to form an Analytics Center of Excellence that can deliver pan-organization actionable insights, and simultaneously explore the potential of emerging technology like machine learning and cognitive computing. Similarly, solutions providers need to proactively explore and offer pan-organization solutions that address organization-wide challenges.
You can hear more from Nikhil, along with other industry leaders, at the Big Data and Analytics For Banking Summit in New York this December. Click here for the full agenda.