Harnessing all of the data your company holds can unlock an early warning system for risk, but building out a risk and data analytics program can be tricky. We sat down with Risk Analytics and Automation expert Christopher Tocin from M&T Bank to learn how he’s building a scalable risk analytics program. We discussed topics including aggregating indicators across all risk types to mine for patterns and anomalies, enabling a consistent approach to risk mitigation, providing a single view of risk, and driving increased efficiencies while ensuring compliance.
Chris shared how he built his program from the ground up and discussed everything from data team development to managing change across an organization.
The Stepping Stones to Develop a Team
When Chris set out to build a data team from scratch, it was essential to start with the right data and the right people for quick wins. When adding talent profiles to the team, he started with a strategy persona, someone who can create a plan to set the stakeholders up for success and devise a data system. Adding someone like this to your team can help set up foundational ground-level work regarding policy, procedure, and regulatory concerns.
“The data is only as good as the story you are telling, and you want someone who understands the industry’s intricacies,” Chris stated, “the right person will know the players in the banking sector, where the data is, and how to speak the language.”
Finding early adopters or internal data customers within your organization can come from places you have already been. When Chris was looking for early-stage internal adoption, he went back to his roots in BSA/AML. Understanding his limited early-stage resources and strategically applying them allowed Chris to offer help where that group was devoid of time and workforce. In exchange for impactful early stage solutions to the BSA/AML teams, Chris was able to secure promised growth and buy-in across the larger organization.
Change Management Across An Organization
Creating a new department comes with challenges, the first one being where to show initial value. To answer this, Chris began with a simple data set: looking at customers over a certain age with no online banking presence, making them more susceptible to fraud.
By discovering these potentially compromised accounts, Chris’s team had the opportunity to discuss risk management mitigation with his customer-facing teams. In doing so, these teams were given a chance to not only mitigate risk but cross-sell different products offered by the bank. The M&T customers would often reach out with a phone call thanking M&T for their great customer service and would often reinvest these re-discovered funds back into the market via M&T services.
By creating these small wins early in the process, Chris and his team built relationships with risk management departments and first-line risk officers across the organization. Instead of self-advocating for his team, these individuals would tell the story of their wins, and that helped Chris secure future success. Doing so brought the data team closer to their goal of wanting to “Live In The Problem” and be proactive rather than reactive to risk mitigation.
From Data Insight to Analytic Automation
Using the small wins to expand upon, Chris and his team could begin identifying existing and new risks quicker while validating assumptions made within data.
When talking about data acquisition, Chris stressed that he looked for data reliability instead of critiquing data quality. Setting up Python scripts to automatically run over certain data sets resulted in unearthing unreliable data points. Instead of offering criticism on the inconsistent data, Chris provided a data reliability analysis to stakeholders so that they could confidently weigh their data-driven decisions. Chris capitalized on the growth stage of his team by using his analytics team as a research & development team so they could experiment with techniques in data acquisition and insight execution. Once a hypothesis was proven, information would then move onto the automation team that created the continuous monitoring or exception processing system.
This set-up eventually led the team from foundational data curation to helping stakeholders understand the need for automated analytics, and will eventually lead to opportunities for robotics process automation, and finally, machine learning and artificial intelligence. The analysis can be grueling, but having an ultimate goal makes it easier to map out a strategy.
The Effect of the Pandemic
The COVID-19 pandemic served as a development catalyst for Chris and his team. The pandemic helped with their growth and success as it forced them to change their thought process from focusing on speed to focusing on quality. Because of the valuable resources and insights this team was generating during the pandemic, the team also experienced increased engagement from other departments within M&T. As a result, his team created a standardized intake form that assessed the level of risk, money loss, and regulatory concern, enabling initiative prioritization.
In the end, this increased engagement accelerated the need for the data team to create a system for processing requests that proved helpful during the pandemic and foundational into the future.
Some Key Take-Aways for All of Us
Chris has done an amazing job of challenging the mindset of “that is just the way we have always done it.” He wants to turn people into critical thinkers instead of button-pushers by not simply stating the facts from the data but allowing the stakeholders to draw their conclusions.
Chris wished he knew at the beginning of the process that quick wins were not going to be plenty, but COVID threw his team into the spotlight and initial solutions showed value.
While, for most, change is scary, and it can take time to show value, you have to have the courage to start something new and stick to it. Chris shared a quote from his grandfather, “Be brave enough to suck at something new.”
Want to know more? Watch the full recording from the webinar to hear the full discussion between Chris and I here.
PerformLine 
Gianna Kennedy 