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GENERATIVE AI IN BANKING: USE CASES, ETHICAL IMPLICATIONS, AND MORE

 * Anand Subramaniam

 * Last updated on: July 25, 2024

The financial sector, long known for its cautious embrace of innovation, is
witnessing a paradigm shift with the advent of generative AI. This
transformative technology, capable of creating entirely new data or modifying
existing information, holds immense potential to revolutionize every facet of
banking.
Traditional AI in banking has primarily relied on supervised learning
algorithms, adept at recognizing patterns in historical data. These algorithms
excel at tasks like fraud detection and credit scoring, but they struggle with
tasks requiring creativity or the generation of entirely new content.
Generative AI, on the other hand, bridges this gap. By leveraging techniques
like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs),
generative AI can create realistic and novel data – be it text, code, or even
images. This power of generative AI unlocks a plethora of use cases in banking.
In this article, we’ll be understanding these diverse use cases of generative AI
in banking and the profound impact these are poised to make in the banking and
financial services landscape.


GENERATIVE AI AND ITS USE CASES IN BANKING

Generative AI empowers banks to venture beyond automation, fostering a new era
of personalized experiences, proactive risk management, and a more inclusive
financial ecosystem. Let’s explore some ingenious use cases of generative AI in
banking:
1. Personalizing Customer Experiences
Generative AI can be harnessed to create individualized financial products and
services. Gen AI-powered chatbots can analyze a customer’s spending habits and
risk profile, and then help banks to craft personalized investment
recommendations or loan offers. This level of customization fosters deeper
customer engagement and loyalty.
2.Transforming Contact Center Management
Contact center agents face information overload, difficulties in interpreting
customer sentiments, and limited call intelligence leading to burnout and poor
service quality. Gen AI can elevate service quality by analyzing past customer
interactions, detecting sentiments, providing real-time support, building a
collective knowledge bank for ready reference, and much more.
3. Revolutionizing Loan Underwriting
The loan application process is often cumbersome and time-consuming. Generative
AI can streamline this by guiding applicants through the process with chatbots
that can answer questions, gather information, and even pre-fill forms based on
the customer’s financial history. Additionally, generative AI models can analyze
vast datasets, including alternative data sources like social media activity, to
create more holistic creditworthiness assessments, potentially expanding access
to credit for underserved populations.
4. Simulating Customer Journeys and Product Design
Generative AI can be used to simulate customer journeys across various
touchpoints within the bank. This allows banks to identify potential pain points
and areas for improvement, leading to a more streamlined and user-friendly
banking experience. Additionally, generative AI can be used to design and test
new financial products before launch, ensuring they meet customer needs and
expectations.
5. Automating Report Summarization and Document Review
Sifting through mountains of financial reports and legal documents is a
time-consuming task for bankers. Generative AI can automate the process of
summarizing key information and highlighting critical sections, allowing bankers
to focus on strategic analysis and decision-making.
6. Anti-Money Laundering and KYC Compliance
Generative AI can be a powerful weapon in the fight against financial crime. By
creating synthetic data mimicking suspicious financial activity, generative
models can train AI systems to identify money laundering patterns with greater
accuracy. Additionally, generative AI can automate the analysis of customer data
for KYC checks, streamlining the customer onboarding process while strengthening
compliance measures.
7. Detecting and Mitigating Cybersecurity Threats
The financial sector is a prime target for cyberattacks. Generative AI can be
used to create synthetic data mimicking malicious cyber activity. This data can
then be used to train AI systems to detect and prevent cyberattacks in
real-time, safeguarding banks and their customers from financial losses and data
breaches.
8. Microfinance and Alternative Lending
Generative AI can be used to develop more accurate creditworthiness assessments
for individuals who lack traditional credit histories. This can open doors to
microfinance and alternative lending opportunities, empowering underserved
communities to access financial products and services that can help them build a
brighter future.
9. Financial Inclusion for Remote Populations
Generative AI can be harnessed to develop low-bandwidth, mobile-based financial
applications tailored for remote populations with limited access to traditional
banking infrastructure. These AI-powered applications can offer basic financial
services such as digital wallets, micro-payments, and savings accounts,
fostering financial inclusion for all.



GENERATIVE AI AND THE HUMAN FACTOR

While generative AI offers undeniable benefits in terms of efficiency and
automation, it’s crucial to emphasize that it doesn’t replace human expertise in
banking. Instead, it serves as a powerful tool to augment human capabilities.
For instance, AI-powered chatbots can handle routine customer inquiries, freeing
up human bankers to focus on complex financial advising and relationship
building.
Furthermore, generative AI can empower human analysts by providing them with
richer data insights and comprehensive reports. This allows bankers to make more
informed decisions and deliver exceptional customer service.


RESPONSIBLE DEVELOPMENT AND DEPLOYMENT OF GENERATIVE AI

The transformative power of generative AI comes with its own set of ethical
challenges and considerations. Biases present in training data can be amplified
by generative models, leading to discriminatory outcomes. To ensure fairness and
transparency, banks must carefully vet their data sources and implement robust
bias detection and mitigation techniques.
Additionally, the use of synthetic data raises concerns around explainability
and accountability. Banks need to develop clear frameworks for explaining how
generative models arrive at their outputs, ensuring responsible use of this
technology.


WHAT DOES THE FUTURE HOLD?

Generative AI presents a transformative opportunity for the banking sector. By
fostering innovation, streamlining processes, and enhancing customer
experiences, it has the potential to redefine the way banks operate. However,
successful implementation requires a multi-pronged approach. Banks must invest
in building a strong foundation of ethical AI practices, prioritize human-AI
collaboration, and continuously adapt to the evolving regulatory landscape.
As generative AI technology continues to mature, its impact on banking will
become even more profound. By embracing this technology thoughtfully and
responsibly, banks can position themselves for a future characterized by
increased efficiency, unparalleled customer service, and a more inclusive
financial ecosystem.
KANINI is a digital transformation enabler with extensive expertise in
implementing technology solutions across the banking and financial services
industry. Our experts are well-versed in AI & Generative AI technologies, and we
can help you implement them for your business. Want to know more? Reach out to
us today!

AUTHOR



ANAND SUBRAMANIAM



Anand Subramaniam is the Chief Solutions Officer, leading Data Analytics & AI
service line at KANINI. He is passionate about data science and has championed
data analytics practice across start-ups to enterprises in various verticals. As
a thought leader, start-up mentor, and data architect, Anand brings over two
decades of techno-functional leadership in envisaging, planning, and building
high-performance, state-of-the-art technology teams.


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