Financial institutions face mounting pressure to reduce fraudulent transactions and illegitimate insurance claims, as well as to identify and report money laundering activities, all of which can result in substantial financial losses and damage to their reputation. Fraudsters, armed with state-of-the-art technologies, are persistently targeting the finance sector, which is often perceived to be conservative and slow to adapt to modern tech solutions.
Consequently, these constant attacks contribute to the finance industry incurring hundreds of billions in losses annually.
In order to respond to these threats, financial institutions need to move beyond hard coded and hand crafted rules to a new era of building applications using the latest advances in cloud computing and AI.
While AI improves anomaly detection, and reduces false positives, it's too early to dismiss rules. Regulators and financial institutions still rely on rules for known risks. Building and maintaining standalone AI models pose challenges, so many institutions combine rules and machine learning in a hybrid approach.
Many believe that the answer to this challenge is hyperautomation. Hyperautomation is often seen as a blend of Robotic Process Automation (RPA) tools and AI. However, as mentioned earlier, what often gets overlooked is that finance relies on complex rule-based systems to handle fraudulent payments and transactions in addition to AI.
In the past, rule based systems were often implemented by Intelligent Decision Systems (IDS) - but these are hard to combine with AI. So, what is the answer?
So let’s revisit what we need in order to achieve all the benefits of hyperautomation in the finance market.
First, let's talk about RPA. RPA is fantastic at automating manual tasks, such as streamlining documentation management, customer care services, market automation etc.. These processes are often slow by nature and often require user inputs. There are many tools in the market that excel at these tasks and they provide extreme benefits to organizations that use them.
It's often said that you need to learn how to walk before you can run. RPA handles the "walking" part extremely well, making simple routine tasks easier. But here's the catch: simply extending RPA may not be enough to get you running.
When it comes to handling more complex, and faster tasks – the story changes. To achieve that, you need what we call "cloud-native" tools. These tools allow for infinite scalability, a crucial factor for rapid operations. Most of the RPA tools are not designed that way.
Another issue is that RPA tools aren't built for making complex decisions. RPA tools mostly deal with one input at a time, deciding whether to proceed to the next phase of processing or not. But complex decisions require a deeper understanding and the ability to branch into different decisions based on previous steps' outcomes.
On the other side of the spectrum, IDS in its own right is not a simple thing to use, they are often used by experts that can input these complicated things in a complicated way.
So, you wonder what is the answer? It should be, as we look at above, some combo of IDS, RPA and AI, built by cloud native tech and simple to use - with help of low code tools on top. Sounds familiar? Sounds like FraudAway.