Top 8 Use Cases of Machine Learning in Banking.pdf

Learn about top use cases of machine learning in banking sector.

2. Introduction
A research by the Economist Intelligence Unit states that 85% of banks have a well-defined AI
adoption strategy. Artificial intelligence and machine learning technologies were once costly and
dangerous to employ, but they have now evolved to the point where banks are looking to use them
to improve their core business operations. Financial institutions have been able to fully utilize
machine learning capabilities because they have made significant investments in cloud
technologies and the upgrading of their key legacy systems over the past ten years. This has allowed
them to overcome technological constraints that previously prevented the adoption of machine
learning consulting services.

This blog well describes the role of machine learning in banking and some important uses cases of
it.

3. In banking, what does machine learning mean?


Using the massive databases that banks gather, machine learning can be used to the banking
industry to produce useful insights. Banks can use machine learning models to process and analyze
data, such as transaction histories, chat logs with bank staff, and corporate paperwork, to gain a
better knowledge of their customers and internal operations. Financial firms may enhance client
interaction, expedite fraud detection, optimize credit underwriting, and increase regulatory
compliance by implementing machine learning in banking.

4. 8 popular Use Cases of Machine Learning in Banking:



Let’s now talk about some of the most effective use cases of machine learning in the banking sector.

1. Credit underwriting
Well-designed machine learning models are far more accurate at estimating borrowers’ risk than
historical credit risk models like FICO. Banks may reach out to underrepresented client groups and
approve more loans more quickly while lowering risks by utilizing machine learning engines.

5. 8 popular Use Cases of Machine Learning in Banking:



2. Fraud detection
In the last 24 months, 46% of firms have encountered fraud, corruption, or other economic crimes,
according to PwC’s Global Economic Crime and Fraud Survey 2022. Better fraud protection
methods are urgently needed by financial institutions due to the considerable increase in
transactions brought about by the accelerated use of online banking and payment digitization.

Financial institutions can virtually instantly adjust to fraudulent tactics that are always changing
thanks to machine learning. Organizations are able to discover hidden links among a multitude of
data points and promptly spot anomalies thanks to sophisticated deep learning models that were
trained on massive datasets that are typically available to banks.

6. 8 popular Use Cases of Machine Learning in Banking:



3. Excessive customization
Delivering exceptional client experiences is becoming one of the key success criteria in the banking
industry, and machine learning can give banks a far better understanding of the needs, wants, and
behaviors of their customers. This enables financial institutions to enhance customer loyalty,
provide AI support for wealth management, personalize their offers more and more, and meet
consumer expectations instantly.

For instance, mobile banking apps that incorporate machine learning-based budgeting features can
assist users in making wiser financial decisions. A machine learning system can recognize user
spending patterns and provide strategies to enhance budgeting based on the history of
transactions.

7. 8 popular Use Cases of Machine Learning in Banking:



4. Automation of tasks
Bank documentation and audits are infamously laborious but necessary chores that every bank
must complete. Auditors often go through documents for hours on end to make sure they comply
with regulations. Banks can save thousands of billable hours by using machine learning algorithms
to review thousands of pages and identify what’s relevant.

8. 8 popular Use Cases of Machine Learning in Banking:



5. Strengthening of cybersecurity
Finance and insurance have been the top targeted industries for cyberattacks for the fifth
consecutive year, according to an IBM report from 2022. Given the vast quantities of extremely
sensitive data banks possess, a significant hack has the potential to be catastrophic. Furthermore,
as the arsenal of cybercriminals is growing and developing more quickly than ever before,
depending just on conventional security measures is no longer an option. Because of this, financial
institutions are using machine learning more and more to improve their security.

9. 8 popular Use Cases of Machine Learning in Banking:



6. Managing risks
In the banking industry, simulation frequently serves as the primary tool for decision-making. Not
only is an accurate evaluation of a bank’s resilience to specific economic shocks required by
regulations, but it also serves as a potent strategic tool. Even though the conventional top-down
method of simulation has shown to be rather successful, it is unable to account for minute but
potentially significant details in the data. However, non-linear correlations between industry and
financial data from banks can be found by fine-tuning machine learning models.

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7. Chatbots
Because natural language processing has advanced so dramatically in the last ten years, virtual
assistants are now a dependable customer support option for many banks. But modern virtual
assistants are more than just answering basic consumer questions; they can also provide
increasingly tailored and helpful financial advise.

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8. Processing of documents
Classifying documents is a crucial yet infamously labor-intensive activity for financial firms.
Financial document labeling, classification, and organization times can be significantly reduced by
machine learning. Additionally, machine learning models can assist with paper documents by
utilizing optical character recognition (OCR).

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Conclusions
Machine learning has prospects for gaining insights, enabling digital transformation in banking,
and extracting value from the vast amounts of consumer data. Financial institutions must be
prepared to redefine data collecting and processing standards, reorganize organizational
workflows, and assist staff in adapting to change if they are to fully grasp the potential of machine
learning.