We surveyed 200 financial institutions to learn how they prevent fraud
Artificial intelligence (AI) and machine learning (ML) make the banks that use them highly competitive. They’ve invested in advanced learning systems to automate and streamline their operations and give them a competitive edge.
You may have read previously on this blog that Brighterion collaborated with PYMNTS.com to analyze how AI and ML are being used by financial institutions (FIs) across the U.S. We surveyed 200 financial executives from banks and credit unions with assets ranging from $1 billion to over $100 billion to get a feel for the industry as a whole. In a series of reports, we’ve presented analysis of the 12,000 collected data points. Today we’re highlighting findings from the first report, The AI Gap: Perception Versus Reality in Payments and Banking Services.
We learned the six most common machine learning tools used by financial institutions:
- Business rules management systems (70.5%): enable companies to define, deploy, maintain and monitor information based on a predetermined set of criteria
- Data mining statistical methods (59.5%): extract trends and other relationships from large databases
- Case-based reasoning (32.0%): an algorithmic approach that uses the outcomes from past experiences as input to solve new problems
- Fuzzy logic (14.5%): traditional logic typically categorizes information into binary patterns like black/white, yes/no or true/false; fuzzy logic presents a middle ground where statements can be partially true and partially false, accounting for much of humans’ day-to-day reasoning
- Deep learning/neural networks (8.5%): technology loosely inspired by the structure of the brain, with a set of algorithms that use a neural network as their underlying architecture
- AI system with intelligent agents (5.5%): personalize, self-learn and adapt to new information
The top four use cases for learning systems were supporting banking services (79.1 percent), enhancing payments services (53.7 percent), customer life cycle management (46.2 percent) and credit underwriting (42.5). Banks also reported using machine learning for compliance and regulation, preventing internal fraud, merchant services, collections and supplier onboarding.
These findings also revealed a gap: financial institutions aren’t accessing true AI with these tools. While the above adoption rates may seem high, most are using inefficient systems.
FIs have invested billions of dollars in machine learning systems that are largely manual and repetitive, often using outdated rules to flag violations of anti-money laundering (AML) regulations. These systems have been largely ineffective at curtailing money laundering and, as a result, regulators in the United States and the European Union have issued more than $340 billion in fines for non-AML compliance since 2009. Additional costs include consultant fees and armies of back office agents.
Brighterion developed a solution to this problem, called Smart Agents . Learning and making real-time observations from interactions with human users, Smart Agents apply this knowledge to create virtual representations of every entity with which they interact, building a digital profile that optimizes customer-facing payments and banking services. This allows FIs to offer personalized financial and payment services. If there are 200 million cards in an ecosystem, there will be 200 million Smart Agents analyzing and personalizing their services to a degree that other ML systems cannot accomplish.
Financial institutions are uncertain about specific aspects of AI and ML technology, and yet an overwhelming majority of them have invested in it and are planning on investing more in the future. Regardless of what they have adopted and whether banks are as AI-capable as they say they are, it is undeniable that they are satisfied with their investments in these systems. We also heard that most of those surveyed felt they already had AI and, once informed, were largely interested in learning more about it.
Is your financial institution invested in AI? Are you getting the full benefit of what’s available? Get the full report to learn more.