How to use AI and communication to break down data silos in financial crime prevention

Overcoming the challenge of organizational silos in financial institutions requires a combination of deploying AI technology and implementing process changes for collaboration 

In today’s complex financial landscape, the isolation of departments and data within organizations – known as “silos” – presents a significant challenge to effective financial crime prevention. A recent webinar hosted by SymphonyAI explored this critical issue, bringing together experts from across the industry to discuss how breaking down data silos can amplify efforts to combat financial crime. 

The scale of the problem is staggering. According to research, 55% of companies have siloed teams, while 54% of financial institution leaders see data silos as a significant barrier to innovation and maintaining a competitive advantage. Perhaps most alarmingly, Fortune 500 companies are estimated to lose over $31.5 billion annually by failing to share knowledge across teams. 

South African bank Absa’s Managing Executive for Strategic Change in Group Compliance  Robert Benvenuti highlighted the pervasiveness of silos in large organizations: “My experience has been in big organizations… We all are run as big silos. It’s almost impossible to run an organization of 40,000 people and not compartmentalize ourselves into silos.” 

The key, according to Benvenuti, is not necessarily eliminating silos entirely, but rather “creating linkages between some of those silos so that there’s flow between them.” This approach recognizes the practical realities of large organizations while still addressing the critical need for improved collaboration and data sharing. 

Bridging the gap: Strategies for breaking down data silos in financial services 

While the challenges of data and organization silos are significant, there are proven strategies for breaking them down and improving collaboration across teams. Meghan Palanza, AML product manager at Symphony AI, emphasized the importance of communication. This simple yet effective approach can yield significant benefits. Palanza shared an example from her experience, where compliance teams normally don’t have all the data they require: “[The company] wanted IP addresses, they wanted media access control address, they wanted malware information… You need to reach out to your IT team and start having those conversations.” 

Software products can help support inter-team communication and workflows. However, the software must be developed with acute understanding of existing and ideal workflows. Nimi Patel, head of user experience at Symphony AI, highlighted the importance of bringing users into the product development process: “We have to bring our users into that product decision making as well. And it really does start from understanding them and their challenges.” This user-centric approach ensures that solutions are designed to meet the actual needs of those working to prevent financial crime. 

Absa’s Robert Benvenuti shared a powerful example of how breaking down silos can lead to tangible improvements in financial crime prevention: “We put in a central customer risk rating model at Absa… As part of that, we didn’t understand some of the decisions we might have made and the impact it’s had on the business in terms of how we suddenly risk rate a lot of their customers as ‘high’.” This centralized risk rating model initiative forced different parts of the organization to come together and understand the downstream impacts of their decisions, ultimately leading to more effective risk management for the bank. 

The role of technology in breaking down data silos 

As financial institutions pursue bridging data silos to improve financial crime prevention and detection, technology plays a crucial role in facilitating collaboration and improving financial crime prevention efforts. Meghan Palanza highlighted the potential of AI and machine learning (AI/ML)and its result on SymphonyAI’s financial crime prevention solutions with Robert Benvenuti echoing this sentiment, emphasizing the power of AI in tackling the sheer volume of data involved in financial crime prevention: “It’s all around the use of big data, the use of AI and the use of machines to do things that humans just can’t do because the problem is just too big.” 

One specific application Benvenuti cited is the use of AI to reduce false positives in anti-money laundering (AML) efforts, particularly in AML transaction monitoring: “By starting to apply AI to the alerts that are generated by a transaction monitoring system, actually you can shut many of them down and say ‘this doesn’t look risky at all.’ And therefore, let the human focus on [the false positives] that are very risky.” 

SymphonyAI’s Nimi Patel also highlighted the potential for AI to improve information sharing and pattern recognition: “Information sharing is going to be quite a big thing, especially if you want to start being a bit more proactive and identifying the kind of behaviours and patterns upfront.” 

Overcoming challenges and reaping the benefits 

While the benefits of breaking down data silos in financial services are clear, it’s important to recognize the challenges. As Benvenuti noted, “Don’t underestimate the size of the problem or the complexity of the problem.” He advised a pragmatic approach: “Don’t try to overachieve. Celebrate those small wins because some of those small wins really, really make stuff happen.” 

One key challenge is balancing the need for collaboration with regulatory requirements and data privacy concerns. Palanza pointed out: “There could be some regulatory restrictions on data that you can share… Where are you filing? In certain countries, you can’t share what you know for any regulatory reports that are being shared.” 

Despite these challenges, the potential benefits of overcoming data silos for financial crime prevention are substantial. Improved cross-team collaboration can lead to more effective risk assessment, faster identification of emerging threats, and more efficient use of resources across the organization. 

As financial institutions continue to grapple with increasingly sophisticated financial crimes, effective internal and even external data sharing will be crucial to staying ahead of the curve. By fostering a culture of collaboration, leveraging advanced technologies, and focusing on incremental improvements, organizations can significantly enhance their financial crime prevention capabilities. 

The future of financial crime prevention is collaboration and innovation 

Looking to the future, the experts on the panel saw several key trends emerging in the fight against financial crime. Absa’s Benvenuti highlighted the potential for increased collaboration between banks: “I think another trend that I expect to see happen is bigger collaboration between us as banks… Certainly, we all know that in the financial crime space, money laundering space, people move money from bank to bank so you only see a little piece of the jigsaw puzzle.” 

This inter-bank collaboration could lead to more comprehensive and effective financial crime prevention strategies, as institutions share insights and best practices while respecting regulatory and privacy constraints. 

SymphonyAI’s Palanza emphasized the growing role of advanced AI technologies such as Sensa Investigation Hub: “We use predictive and generative AI to address challenges and make investigators lives more efficient.” She noted that technologies like autonomous agents, which were barely on the radar a year ago, are now being actively explored for their potential in financial crime prevention. 

As financial institutions continue to innovate and collaborate, the landscape of financial crime prevention is likely to evolve rapidly. By breaking down data silos, overcoming internal hurdles, and embracing new technologies, organizations can stay ahead of emerging threats and protect their customers and the broader financial system more effectively. 

Conclusion: embracing a collaborative future in financial crime prevention 

Breaking down data silos is not just a technical challenge, but a cultural and organizational one as well. As the experts in this webinar made clear, success ihappens in small steps and requires a combination of commitment to collaboration and embrace of new technologies.  

By fostering a culture of knowledge sharing, using advanced AI/ML technologies, and working to overcome regulatory and organizational barriers, financial institutions can significantly enhance their ability to prevent and detect financial crime. 

Looking into the future, it’s clear that the most successful organizations will be those that can effectively break down data silos and create a more integrated, collaborative approach to financial crime prevention. The challenges are significant, but the potential benefits – both for individual institutions and the financial system as a whole – are immense. Together, we can build a more resilient and secure financial system for all.