Artificial intelligence (AI) is rapidly transforming the
operations of financial institutions, making them more efficient and
competitive.AI can help to accelerate revenue growth, reduce risk and cut
costs, delivering tremendous value to financial firms. Its applications in
finance are extensive, Yeo Hwee Theng, data scientist at DataRobot, said in a
webinar co-hosted with FinanceAsia in June.In a fully AI-driven organisation,
AI does the heavy-lifting, allowing financial institutions to focus on their
essential work to better serve clients, Nina Xing, another data scientist at
DataRobot, said in the same webinar.
In the case of anti-money laundry (AML) practices, AI can
help financial institutions save a substantial amount of capital. The estimated
return on investment can range between $2 million and $5 million, according to
studies by DataRobot.
AML is important for reducing regulatory risks and
increasing brand awareness. However, the rule-based models deployed by most
banks now are not the most ideal AML solutions. Machine learning models can be
introduced to replace or supplement existing rule-based models.
Companies can build rule-based models across the transaction
monitoring process in their AML practices. The models gather customer
background information and verify if they are legitimate ones when they onboard
customers, Yeo said.
Banks then monitor the transactions. Some of them will be
flagged as suspicious by the rule-based models after alerts are set up.
“But the problem with rule-based models is that they are
rigid and slow at reacting to customer behaviour changes. And as a result, many
of the alerts are actually false alarms. And there will be no suspicious
activity reports filed after the investigation,” she said.
Only around 40% of the alerts involve complex situations.
Studies have also shown that false rates can be over 90% with rule-based models
and huge resources are wasted during the manual investigation process, she
said.
“[In comparison], machine learning models can filter out the
false alarms and reduce the number of cases that requires manual review. Thus,
they increase the operational efficiency and reduce costs,” she said.
When it comes to ensuring that the models are always up to
date and relevant, Yeo added that AI models can be retrained periodically with
the latest transaction data to capture changes in market condition and customer
behaviour. The retraining process, which is fairly straightforward, can be
scheduled to run regularly.
AML only demonstrates one of the countless applications of
AI in finance. On the buy-side of the business, AI can be leveraged in areas
such as asset allocation, factor model build-up, and smart beta strategy
discovery, Yeo said.
Sentiment analysis is another good example to showcase how
natural language processing, a part of the machine learning techniques, helps
to drive business. It can help capture signals that traditional quant
techniques find it hard to do, she said.
THE CHALLENGES
It is important for financial institutions to embark on the
AI journey as soon as possible, which starts when they establish awareness and
acceptance of AI across the organisation.
As they move around the AI maturity curve, they will develop
more use cases, driving efficiency and standardisations across the end-to-end
pipeline while accelerating their data science capabilities.
When many use cases are generated, organisations will start
to “democratise” data scientists, allowing staff like business analysts to work
on AI initiatives. These are the key milestones in their journey to becoming
fully AI-driven, Xing said.
However, automation is not easy to achieve, especially
because people need to trust the model that they built in many cases.
“People may be skeptical about AI and think that they give
black-box solutions. Others have concern about ensuring the models stay
relevant to the ever-changing business environments and are compliant with
global regulators,” she said.
The technical details present other challenges. Financial
institutions need to know the model accuracy, compare performances between
models, and understand the trade-off between different aspects such as speed
versus accuracy.
They should also be able to explain the model structure and
understand the kind of data needed in the pre-processing stage. Measures have
to be put in place to prevent a drift in data.
“All these are important questions to validate that your AI
is not a black box. And we are not relying on unexplained models to make
critical business decisions,” she said.
They are difficult yet important steps to take to reap the
ultimate benefits of AI and stay ahead of the curve in the rapidly changing
business environment.
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