We are pleased to announce Dan Dica has been appointed the new CEO of Lynx. Lynx is a software company that specializes in AI-based fraud and financial crime prevention solutions.Dan will oversee Lynx’s businesses to take them to new heights growth and global success, turning Lynx into a global leader in AI for fraud and financial crime prevention. […]
More FI’s onboard customers and operate digitally than they have ever done before, this digital adoption is only going to accelerate as more people expect more personalized, transparent and interconnected services from their financial institutions. We anticipate at Lynx that several players will operate in the intersection of finance and social, likely these players will enable embedded finance and push forwards instant realtime seamless financial experiences through super apps.
It has long been the case that Fraud Prevention, Anti Money Laundering and Cybersecurity are separate solutions with their own specific use cases that they address.
Fraud Prevention has somewhat sat between the two trying to take the best from both sides, but if you’re a financial institution it was expected that you would buy an isolated solution to address AML, an isolated solution to address Fraud, and an isolated solution to address Cybersecurity.
Triforce: Fraud Prevention, Anti Money Laundering and Cybersecurity
But all that’s about to change.
More FI’s onboard customers and operate digitally than they have ever done before, this digital adoption is only going to accelerate as more people expect more personalized, transparent and interconnected services from their financial institutions.
We anticipate at Lynx that several players will operate in the intersection of finance and social, likely these players will enable embedded finance and push forwards instant realtime seamless financial experiences through super apps.
Just as financial institutions are going through their biggest changes yet through digital transformation they expect their technology stack to progress with them and the vendors they use to follow suit. We’re here to tell you many can’t, but we at Lynx have the vision and agility to do so.
Datos Insights agree, and recently wrote a report based on the current state of Fraud and AML Machine Learning Platforms.
The key take away from the report:
What does a next generation Fraud Prevention and AML ML solution look like and who will own it?
It is perhaps easier to answer the second part of the question first, likely this will be owned by the cyber fusion centre.
There is an additional transformation happening within financial institutions, mainly the cyber fusion centre. This is the coming together under the cyber security division of fraud, AML, and cyber security tools to increase:
As to what will the solution look like. We expect that it will evolve AML digitally, bring fraud prevention forwards in terms of machine learning and forensics with overarching key components shared across the tools. These are:
Attackers have identified that they can scale up their operations by using the digital channels, automation and AI against the FI.
The attacker is able to harvest, purchase, generate real and or synthetic identities on mass and automate their attack with the financial institution.
The impact is that data that was used historically to go through KYC and due diligence doesn’t have the same level of surety and human belonging as it’s fake or stolen. As such onboarding has to evolve to make use of digital data that Fraud Prevention and Cyber Security teams have used for some time.
For example, you can identify the device, location, user agent and cookies of the application to understand if they are part of a compromised list of attribute associated to an organised crime group trying to build a network of mule accounts in your financial institution.
Or you can make use of intelligence networks to identify deliberately obfuscated links between criminals and the applicant.
A big problem for both Fraud and AML is that analysts are inundated with alerts based on rule matches. By using highly effective machine learning (ML) models you can reduce the amount of false alerts by up to a factor of 10 compared to other ML solutions using unsupervised learning. The implementation of supervised machine learning improves the accuracy of the rules using a risk score. Thus reducing the amount of people needed, and improving the time the analysts spend investigating.
Another problem is the capability to get a holistic view of the user and their associated entities, this can be solved with combined advanced entity link analysis tools. Thus enabling analysts to come to quicker decisions on alerts and find connected undetected criminal networks.
The future problem will not be know your customer or know your business, instead it will be know your identity and aversaries will continue to innovate thus obfuscating and adding layers to the identity they pose to be.
Solutions best prepared to seamlessly work with embedded finance, digital identity, intelligence networks and were born out of machine learning will outperform legacy point solutions.
That’s why we at Lynx are forging a path for our solutions to digitally enrich and converge changing the narrative from know your customer to know your identity.
Why not find out more and watch us as we evolve to bring fraud, aml and cybersecurity together and a next generation identity.
Let us assist you in the fight against fraud and financial crime.
