Blog post by Julien Mansourian, Strategy and Transformation Executive, Oracle Financial Services Analytical Applications.
FinTech is going through a significant innovation change, which is impacting the overall banking operating model and consequently the current techniques used to comply with regulations.
Exploring sophisticated, innovative and cost-effective technology platforms to meet the regulatory compliance requirements is in motion and banks will certainly leverage the Artificial Intelligence (AI) path going forward.
One good example is Deutsche Bank - leveraging AI to reduce the workforce across the board:
"The rise of artificial intelligence (AI) and automated technology is set to have a huge impact on the future workforce of Deutsche Bank, with a "big number" of staff inevitably impacted because they already resemble robots," chief executive John Cryan has said. Link to article.
The second example is HSBC, who is partnering with an AI startup to automate AML detection flow. Link to article.
So, it certainly makes sense for banks to already implement an AI-Centric AML/Sanctions system than implementing a Rules-Based Financial Crime application now and rework or replace it later.
AI is the breathtaking expansion of digital data that FinTech has generated over the last 20 years. This heavy data digitization pushed the former data analysts to become data scientists to more focus on developing new techniques to retain, categorize and analyze the information. In parallel, the evolution of Big Data coupled with exponential growth in Cloud Computing including PaaS, SaaS, IaaS, Hardware Stacks and Information Processing platforms have enabled engineers to design algorithms that are no longer bound by the parameters in their code. Moreover, the Real-Time decision making has never been as important as it is today in our day to day business.
So, in a nutshell, AI is a branch of computer science that aims to create intelligent machines. It has become an essential part of the technology industry. AI is the simulation of human intelligence processes by machines. These processes include learning (the acquisition of information and rules for using the information), reasoning (using the rules to reach approximate or definite conclusions), and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.
Today, many RegTech companies are improving their regulatory solutions to include innovations such as Cloud, AI, Machine Learning, Automation and Cognitive to follow the market trends.
Up to now, most of the banks used a rules-based detection approach to AML and are gradually moving away from a rule-based method to an AI approach. An AI driven system that can gather data instantly and be programmed to make decisions when given a set of simple facts will no doubt improve the AML layer.
A rules-based approach to AML would, for example, flag cash transactions over a certain currency amount, block transactions to certain countries, use customer data to select accounts for additional monitoring, and categorize merchant accounts based on prior transactions. These types of systems are already widely used, but they require a significant amount of bank resources to review the transactions that are flagged or blocked to weed out false positives. In addition, a rules-based approach to AML will be unable to adapt to changes in criminal behavior designed to evade detection.
An AI approach to AML, by contrast, does not require developers to establish rules that identify potentially criminal transactions. Instead, the system would be trained to identify such transactions over time by analyzing a staggering array of factors. These could eventually come to include where a customer opens an account relative to their home address, what time of day an account was opened, duration between transactions, patterns among the merchants where a customer makes transactions, relationships between other customers of those same merchants, whether a customer uses a mobile telephone, what communication channel a customer uses to contact the bank and even changes in a customer's social media presence. The factors that AI can evaluate are limited only by the available data.
AI can identify patterns and connections among the data that humans cannot hope to recognize. Using this information, the AI would then monitor every transaction processed by a bank and predict whether each one is or is not criminal. The accuracy of such a system would be significantly higher, and the resources needed to monitor the output significantly lower, than with a rules-based system. Importantly, an AI system would continually improve its accuracy automatically.
For OFAC compliance, as with AML systems, AI would drastically improve detection. This is particularly important because banks are strictly liable for any transaction involving an entity on the OFAC sanctions lists. It has long been routine for bank systems to block attempts by persons on these lists to open accounts or by existing account holders to initiate a transaction with such people. But it is far more difficult for a bank to detect when OFAC controls are intentionally circumvented or the counterparties of transactions are obscured. An AI-based system, however, would be able to prevent these transactions specifically by not relying on defined rules.
We are living in an amazing period where we constantly go through technology innovations, but how far can Artificial Intelligence go?
Our brain is made up of 100 billion neurons and each neuron has 1000 to 10000 synapses that sent electrochemical signals to 1000 up to 10000 other neurons at the speed of 200 mph. That is a lot of processing.
David Deustch predicts with the advent of the quantum computer which measure data in 'qubits' rather than in bits the processing speed alone will be unimaginable compared to today's computers.
Artificial Intelligence is definitely the next logical step of the technology revolution and will probably surpass our intelligence.