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 1. Trinetix
 2. Insights
 3. Generative AI in Banking: Practical Use Cases and Future Potential
    


GENERATIVE AI IN BANKING: PRACTICAL USE CASES AND FUTURE POTENTIAL

AI/ML
6.6.24
Dmytro Ivanov
MACHINE LEARNING ENGINEER
Daria Iaskova
COMMUNICATIONS MANAGER

As artificial intelligence (AI) penetrates operations, streamlines
decision-making, and reinvents every facet of customer interactions across
multiple industries, it’s also having a transformative impact on banking and
finance.

The numbers speak for themselves. McKinsey estimates that across the global
banking sector, AI and generative AI in particular could add up to $340 billion
or 4.7% of total industry revenues annually.

Generative AI use cases in banking are diverse and impactful, including enhanced
customer service, fraud detection, regulatory compliance, and predictive
analytics. At the same time, AI solutions often come with privacy risks that
companies should take seriously from the outset. 

So, how far can AI in banking and finance take businesses, and how to implement
the technology in practice considering existing limitations, specific business
constraints, and the changing market landscape? Let’s explore below.

Table of contents
 * What is AI in banking?
   * The evolution of AI in banking
   * Benefits of AI in financial services
 * Generative AI: a disruptor to financial services 
   * Popular applications of generative AI in banking and finance industry
   * Real-life generative AI use cases in banking
 * What does the future hold for generative AI in banking industry?
 * How to approach AI and generative AI development in banking?
   * Risks and challenges that come with AI adoption
   * Steps to get started with generative AI in banking and finance industry
 * FAQ

Table of contents
 * What is AI in banking?
   * The evolution of AI in banking
   * Benefits of AI in financial services
 * Generative AI: a disruptor to financial services 
   * Popular applications of generative AI in banking and finance industry
   * Real-life generative AI use cases in banking
 * What does the future hold for generative AI in banking industry?
 * How to approach AI and generative AI development in banking?
   * Risks and challenges that come with AI adoption
   * Steps to get started with generative AI in banking and finance industry
 * FAQ


WHAT IS AI IN BANKING?

Before diving into practical use cases, let’s first define AI in banking and
financial services. AI in this sector usually refers to the use of advanced
algorithms and machine learning techniques to automate processes, boost
decision-making accuracy, enrich customer engagement, and refine operational
workflows within financial institutions.




Representing a blend of cutting-edge technology algorithms and tools, artificial
intelligence in banks often encompasses:

 * generative AI
 * conversational AI
 * machine learning 
 * predictive analytics 
 * natural language processing (NLP)
 * robotic process automation (RPA) 
 * computer vision

Productizing these technologies and making them part of digital banking
solutions unlocks numerous benefits, driving innovation and enhancing efficiency
within modern BFSI organizations. Today, this is something businesses mostly
take for granted. But how did we get here?


THE EVOLUTION OF AI IN BANKING

For the past ten years, machine learning and AI in banking have undergone a
myriad of changes. 

Initially, machine learning and natural language processing were employed to
automate routine tasks and enhance back-office operations—this phase saw the
development of AI-powered chatbots for customer service, automated document
processing for loan approvals, and algorithmic trading systems for financial
markets.

As AI matured, financial institutions started leveraging more sophisticated AI
applications to improve decision-making processes. Advanced predictive analytics
and data-driven insights enabled banks to assess credit risk, detect fraudulent
activities, and optimize investment strategies. 

The adoption of AI in banking accelerated further with the integration of big
data analytics and cloud computing technologies. Banks started harnessing vast
amounts of data from internal and external sources to gain deeper insights into
customer behavior, market trends, and regulatory compliance. AI-driven
recommendation engines personalized product offerings, while automated wealth
management platforms provided tailored financial advice to clients.

Moreover, the rise of regulatory technology (RegTech) solutions powered by AI
helped banks navigate increasingly complex regulatory landscapes more
efficiently. AI algorithms deployed to monitor transactions for compliance
violations, ensure data privacy, and enhance cybersecurity measures bolstered
customer trust and loyalty as digital banking was gaining traction.

Looking ahead, AI continues to drive innovation in banking, positioning
businesses at the forefront of digital transformation and customer-centric
financial services.


BENEFITS OF AI IN FINANCIAL SERVICES

While McKinsey predicts that the largest value AI can bring to finance and
banking lies in improved productivity, the extensive practice of global
financial corporations proves that the benefits technology can bring to the
sector are far more diverse and impactful.

SIMPLIFIED BANKING OPERATIONS

AI revolutionizes banking operations by automating repetitive tasks such as
transaction processing, customer inquiries, and document verification. This
automation reduces manual effort, accelerates processes, improves service
availability, and enhances operational efficiency. 

REDUCED COSTS

AI-driven automation optimizes resource allocation and reduces dependency on
human intervention in routine tasks, leading to significant cost savings for
financial institutions. By automating back-office processes like data entry and
compliance checks, AI minimizes operational expenses and frees up human
resources to focus on more strategic initiatives. 

FASTER AND MORE ACCURATE DECISION-MAKING

By analyzing large volumes of data at high speeds, AI algorithms provide
actionable insights that enable faster and more informed decision-making. For
instance, AI-powered risk assessment models can swiftly evaluate
creditworthiness and detect fraudulent activities, reducing decision-making time
and enhancing accuracy. 

ENHANCED RISK MANAGEMENT

AI-powered risk models continuously monitor transaction patterns, market trends,
and regulatory changes to detect anomalies and mitigate risks in real-time. This
proactive approach improves compliance with regulatory requirements and enhances
overall risk mitigation strategies, safeguarding the financial stability of
institutions and increasing trust among stakeholders.

IMPROVED FINANCIAL WELL-BEING

AI-driven personalized financial services cater to individual customer needs by
offering tailored recommendations and solutions. By analyzing customer data and
behavior patterns, AI algorithms provide insights into spending habits, savings
goals, and investment opportunities. This personalized approach helps customers
make informed financial decisions, achieve their financial goals, and improve
their overall financial well-being.


GENERATIVE AI: A DISRUPTOR TO FINANCIAL SERVICES 

While traditional machine learning and artificial intelligence have demonstrated
efficiency across various aspects of financial management and banking,
generative AI stands out as a true game changer for the industry.

Its capability to generate unique and meaningful outputs from human language
inputs has made this technology particularly invaluable for streamlined customer
service, financial report generation, personalized investment advice, and more.

According to Statista, the banking sector's investment in generative AI is
expected to reach $85 billion by 2030, growing at an impressive annual rate of
over 55%.




POPULAR APPLICATIONS OF GENERATIVE AI IN BANKING AND FINANCE INDUSTRY

There is a common misconception that generative AI applications in banking boil
down to implementing conversational chatbots into customer service. While AI
chatbots are indeed a common use case in the sector, there is much more behind
the technology, and a number of large market players are already taking
advantage of this promising potential.

INTELLIGENT VIRTUAL ASSISTANTS

Generative AI powers advanced a new era of chatbots that handle customer
inquiries with accurate human-like responses. These virtual assistants can
understand and generate natural language, offer personalized support, resolve
issues, and provide 24/7 support, significantly improving customer satisfaction
and operational efficiency. They also reduce the workload on human agents,
allowing them to focus on more complex tasks, and can be integrated across
various platforms such as mobile apps, websites, and messaging services,
ensuring a seamless customer experience.

For example, an online bank might deploy a virtual assistant that uses
generative AI to help customers with tasks such as checking account balances,
transferring money, and providing personalized financial advice. 

FINANCIAL REPORT GENERATION AND TAX AUTOMATION

Generative AI-powered tools automate the creation of comprehensive financial
reports by analyzing vast amounts of data and generating detailed narratives.
For instance, a bank might use AI to interpret commercial loan agreements and
generate financial summaries. This application saves time, reduces human error,
and ensures that stakeholders receive accurate and timely financial insights,
allowing financial analysts to focus on more strategic tasks.

Also utilizing generative AI helps to automate tax administration including tax
form processing, return filing and submission, enabling secure and compliant
business continuity in wealth management, insurance underwriting, asset
management, and retail banking.

Automated wealth management made possible with intelligent tax bots

A BIG FOUR’S STORY

Read case study

PERSONALIZED INVESTMENT ADVICE

Making part of an integrated solution, generative AI helps to analyze individual
customer profiles, market trends, and historical data to offer tailored
investment advice. Dedicated algorithms can simulate various financial scenarios
and generate personalized recommendations, helping clients make informed
investment decisions and enhancing portfolio management. 

For example, a wealth management firm could implement AI to provide tailored
investment strategies and portfolio management for their clients. This
personalized approach not only improves client satisfaction but also builds
trust and loyalty, as customers feel their unique needs and goals are being
addressed.

AI IN BANKING FRAUD DETECTION AND PREVENTION

AI-enabled banking solutions detect unusual patterns and potentially fraudulent
activities by analyzing transaction data in real-time. This application reduces
the incidence of false positives, improves the accuracy of fraud detection, and
enhances overall security, protecting both the institution and its customers
from financial losses.

A credit card company, for instance, might use AI to monitor and analyze
millions of transactions daily, identifying and flagging suspicious transaction
patterns and unauthorized charges. By generating alerts and providing actionable
insights, such AI-driven systems help prevent fraud and mitigate risks
effectively.

RISK MANAGEMENT AND COMPLIANCE

AI reinforces risk management by generating predictive models capable of
identifying potential risks and compliance issues. With its ability to stimulate
various risk scenarios, generative AI can be used to develop mitigation
strategies and ensure adherence to regulatory requirements. This allows
businesses to reduce the burden on compliance officers, improve accuracy, and
ensure timely reporting, thus avoiding costly fines and reputational damage.

For example, a commercial bank might use AI to monitor transactions for signs of
money laundering and other financial crimes. In this case, the technology allows
to analyze transaction patterns and generate alerts for suspicious activities,
helping the bank comply with regulatory requirements and improve overall risk
management strategies.

AUTOMATED DOCUMENT PROCESSING

Generative AI streamlines document processing by extracting relevant information
from unstructured data sources, such as emails and scanned documents. This way,
financial businesses can accelerate KYC (Know Your Customer) procedures and
other documentation tasks, ensure data accuracy and consistency, enhance
regulatory compliance, and improve the customer onboarding experience.

For instance, a mortgage brokerage firm could use AI to automatically extract
and verify information from loan applications, reducing manual processing time,
minimizing errors, and accelerating workflows related to loan approvals and
compliance checks.

Learn how you can streamline enterprise document management using OpenAI’s model
Read article

LOAN AND CREDIT SCORING

AI helps to refine loan and credit scoring processes by generating detailed risk
profiles for potential borrowers. Used in combination with data analysis tools
and dedicated machine learning, it helps lenders make more accurate credit
decisions and offer personalized loan terms. 

For example, a credit union might use AI to analyze a wide range of data points,
helping lenders make their credit decisions and benefit from the best loan
terms. This leads to better risk management, reduced default rates, and
increased access to credit for customers who may have been overlooked by
traditional scoring methods.

ALGORITHMIC TRADING

Machine learning and AI enhance algorithmic trading strategies by optimizing
trading algorithms based on market data. By continuously learning from market
trends and performance metrics, AI can adapt strategies in real-time, maximizing
profitability and minimizing losses.

For instance, a hedge fund might use AI to develop sophisticated trading
algorithms that adapt in real-time to market conditions. This allows for more
sophisticated trading decisions, better risk management, and improved returns on
investment.

ENHANCED FINANCIAL FORECASTING

Generative AI models can handle data extraction tasks that are essential for
building financial forecasting solutions. Using these solutions leads to more
resilient planning and allows financial businesses to identify emerging
opportunities or threats in the market, providing a competitive edge.

A financial services firm, for example, might use AI to enhance its economic
forecasting models. This would help them make better strategic decisions,
optimize resource allocation, and anticipate market movements, leading to more
resilient financial planning and identifying emerging opportunities or threats.

Unlock the power of world-class financial analytics

GET INSPIRED BY A BIG 4’S REAL STORY

Read case study

MARKETING AND CUSTOMER ENGAGEMENT

Last but not least, generative AI algorithms can analyze customer data and
preferences to create personalized marketing content and campaigns. For example,
a digital bank could generate targeted marketing messages, offers, and
recommendations based on customer behavior and predict future needs to deliver
more relevant and timely communications, increasing conversion rates and
customer loyalty.


REAL-LIFE GENERATIVE AI USE CASES IN BANKING

As the applications of generative AI in banking industry are gaining traction,
more widely known global brands are integrating the technology into the core of
their digital solutions.



MASTERCARD

In February 2024, Mastercard launched a cutting-edge generative AI model
designed to enhance banks' ability to identify suspicious transactions across
its network. The technology called Decision Intelligence Pro is projected to
bolster fraud detection rates by up to 20%, with some institutions experiencing
increases as high as 300%. 

Drawing insights from approximately 125 billion transactions processed annually
through its card network, Mastercard leverages this vast dataset to train and
refine the AI model.

J.P. MORGAN CHASE

In March 2024, J.P. Morgan Chase & Co. announced the launch of IndexGPT, an
AI-powered tool designed to provide investment advice to retail clients in Latin
America. This cloud-based service uses advanced AI to analyze and select
financial assets tailored to each client's needs, democratizing access to
sophisticated investment tools. 

With IndexGPT, J.P. Morgan aims to revolutionize financial decision-making and
enhance outcomes for individual investors in the region. 

MORGAN STANLEY

Morgan Stanley also introduced an AI assistant powered by OpenAI’s GPT-4,
enabling its 16,000 financial advisors to access a repository of approximately
100,000 research reports and documents instantly. The AI model is designed to
assist advisors in efficiently locating and synthesizing information for
investment and financial inquiries, providing tailored and immediate insights.

ING BANK

As a major player in the Dutch banking sector, ING used to handle 85,000
customer interactions weekly, but their existing chatbot could only resolve
40-45% of these, leaving 16,500 customers requiring live assistance.

To improve customer experience and enhance their support capacity, the bank
collaborated with McKinsey to develop a generative AI chatbot capable of
providing immediate and tailored assistance. After a soft launch in September
2023, this new AI-powered assistant quickly demonstrated its superiority over
the traditional chatbot by assisting 20% more customers, reducing wait times,
and improving overall customer satisfaction. 

OCBC BANK 

OCBC Bank in Singapore has recently reported that a six-month generative AI
chatbot trial brought them a 50% efficiency lift, streamlining writing,
translation, and research activities. In the past, when the company utilized
technology to assist employees in developing code, summarizing documents,
transcribing calls, and building an internal knowledge base, they achieved a
similar productivity boost.

Currently, OCBC Bank is expecting this in-house AI-based solution to help their
30,000 employees make risk management, customer service, and sales decisions. 

CITIGROUP

Recently, Citigroup leveraged generative AI to assess the impact of new US
capital regulations. The bank's risk and compliance team utilized the technology
to efficiently analyze and summarize 1,089 pages of newly released capital rules
from federal regulators. 

Additionally, Citigroup plans to employ large language models (LLMs) to
interpret legislation and regulations in various countries where they operate,
ensuring compliance with local regulations in each jurisdiction.


WHAT DOES THE FUTURE HOLD FOR GENERATIVE AI IN BANKING INDUSTRY?

Beyond any doubt, the use of generative AI in banking is poised to bring both
expected and surprising changes, leading to an evolution and expansion of AI's
role in the sector. However, significant changes from generative AI in banking
will require some time. 

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Banks are expected to continue investing in generative AI models and testing
them over the next 2-5 years. In the short term, banks will likely focus on
incremental innovations—small efficiency gains and improvements based on
specific business needs. Employees will maintain an oversight role to ensure
accuracy, precision, and compliance as the technology matures.

In line with approaching generative AI for innovation, banks are expected to
utilize the technology to improve efficiency in existing and older AI
applications. Just like that, automating customer-facing processes creates
digital data records that generative AI can use to refine services and internal
workflows. These records can enhance risk management, automate data collection,
and streamline reporting, leading to further digitalization, end-to-end
customization, better client segmentation, and retention.

All in all, the development of generative AI capabilities in banks will depend
on their scale and investment capacity, ranging from in-house solution
development to fine-tuning existing models. But regardless of these constraints,
here are some key areas where generative AI could make a significant impact over
the next years:

 * Personalized financial products

Utilizing generative AI allows financial companies to create tailored financial
products based on individual customer profiles and behaviors, leading to higher
customer engagement and satisfaction. Banks can integrate the technology into
their digital solutions to analyze customer data and market trends and develop
innovative and highly personalized financial products.

 * Enhanced financial forecasting

Making part of dedicated digital assets, generative AI algorithms can improve
financial forecasting by analyzing historical data and current market
conditions, providing more accurate and timely predictions. Financial
institutions can leverage such tools for strategic planning processes and
continuously train AI models with the latest data to ensure relevance and
accuracy in predictions.

 * Employee augmentation

AI can assist employees by providing instant access to information, automating
routine tasks, and generating insights, allowing them to focus on more strategic
activities. In the future, banks should adopt a hybrid approach where AI tools
augment human capabilities and implement training programs to help employees
effectively use AI tools and understand their outputs.

Although the significant transformation that generative AI is likely to bring to
banking and financial services will take some time, coming prepared for the
changes remains a number one priority for businesses aiming to maintain their
competitive advantage in the market. Below, we’ll explain what it takes to
approach generative AI in practice.


HOW TO APPROACH AI AND GENERATIVE AI DEVELOPMENT IN BANKING?

In many aspects, adopting generative AI in banking is akin to any other software
development project: it involves gathering requirements, conducting research,
selecting optimal solutions, deploying, testing, and iterating for improvement. 

However, the banking sector presents unique challenges due to numerous risks and
limitations, especially concerning privacy concerns inherent to generative AI
technology. Therefore, before diving into implementation details, it is crucial
to understand these risks and limitations in full. This way, organizations can
ensure that the deployment of generative AI not only enhances efficiency and
innovation but also prioritizes security and regulatory compliance.


RISKS AND CHALLENGES THAT COME WITH AI ADOPTION

So, below we highlight several significant risks and challenges that financial
institutions must carefully navigate to achieve success with AI in banking and
finance.

 * Data privacy and security. Protecting sensitive customer information and
   preventing data breaches is critical. AI systems rely on extensive data, and
   ensuring robust cybersecurity measures is essential to maintain customer
   trust and comply with regulations.

 * Ethical considerations. AI algorithms can unintentionally propagate biases
   present in training data, leading to unfair outcomes like discriminatory
   practices in lending or decision-making. Banks must implement ethical
   guidelines and audits to mitigate bias risks.

 * Regulatory compliance. Banking operates within stringent regulatory
   frameworks. Ensuring AI adoption aligns with data protection laws, consumer
   rights regulations, and financial transaction laws is crucial to avoid legal
   repercussions.

 * Reliability and accuracy. To aid financial companies and their customers in
   making informed decisions, AI models must produce reliable and accurate
   results. Maintaining this accuracy requires ensuring transparency in AI
   operations and continuously monitoring model performance.

 * Operational integration and complexity. Integrating AI into existing banking
   systems can be complex and resource-intensive. Banks need to ensure seamless
   integration with legacy systems, minimal disruption to operations, and
   effective change management strategies.

 * Scalability and maintenance. Scaling AI solutions across an organization
   requires ongoing maintenance, updates, and support. That’s why banks must
   allocate resources for continuous monitoring, improvement, and adaptation of
   AI models to evolving business needs.

 * Public perception and trust. Building and maintaining public trust in
   AI-driven banking services is crucial. Banks must be transparent about how AI
   is used, protect customer data, and communicate clearly about the benefits
   and risks of AI adoption to maintain customer confidence.

Learn how AI-enhanced risk management helps to build stronger resilience and
compliance
Read article


STEPS TO GET STARTED WITH GENERATIVE AI IN BANKING AND FINANCE INDUSTRY

Considering the challenges and limitations described above, the integration of
generative AI solutions into financial operations requires thorough strategic
planning. Moreover, with each business case being unique and sophisticated, the
decisions related to AI enablement as well as the results expected from
technology adoption always make a difference.

At the same time, the general flow for developing and successfully deploying a
generative AI solution in production often consists of 5 foundational steps
described below.



STEP 1: DEVELOP AN AI STRATEGY

Start by formulating a comprehensive AI strategy aligned with the bank's goals
and regulatory requirements. Define clear objectives for integrating generative
AI, identifying key stakeholders, and establishing governance frameworks.

STEP 2: ASSESS THE CURRENT DATA STATE

Evaluate the quality, security, and reliability of existing data repositories.
Ensure adequate storage capacity and data accuracy necessary for developing and
training AI solutions. Address any gaps in data infrastructure to support the
implementation of generative AI technologies effectively.

STEP 3: PROTOTYPING AND TESTING

Choose an appropriate generative AI model and adapt it according to the defined
objectives. Develop prototypes to validate AI algorithms and assess their
feasibility in real-world banking applications. Conduct thorough testing and
validation to refine the AI model based on performance metrics and user
feedback.

STEP 4: DEPLOYMENT AND SCALING 

Deploy validated AI solutions into operational environments, starting with pilot
implementations to mitigate risks and optimize performance. Scale AI initiatives
gradually across different banking functions, ensuring seamless integration with
existing workflows and systems.

STEP 5: MONITORING AND OPTIMIZATION

Establish continuous monitoring mechanisms to track AI performance, data
quality, and regulatory compliance post-deployment. Implement iterative
improvements based on insights gained from operational feedback and evolving
business needs.

Note that making generative AI initiatives drive measurable outcomes requires
financial companies not only to consider all possible risks and limitations but
also develop a strategic roadmap focused on achieving value in the areas that
hold the most promising potential.

At Trinetix, we provide comprehensive technology guidance and end-to-end AI
implementation support, so that financial companies can focus on their business
priorities and scale market impact. If you are inspired by successful generative
AI use cases in banking, let’s chatand schedule a discovery session where we
could discuss potential applications and limitations for your specific scenario.


FAQ

What are the most practical use cases of AI in banking and finance?
Artificial intelligence in banks is mostly used for enhancing customer service
through chatbots, automating fraud detection, streamlining credit scoring, and
improving personalized financial recommendations, thereby increasing efficiency
and customer satisfaction.
How can financial companies benefit from AI in banking and payments?
Financial companies can benefit from AI in banking and payments by streamlining
transaction processes, enhancing customer service through chatbots and virtual
assistants, improving fraud prevention, and more. AI can also optimize credit
scoring, reduce operational costs through automation, and provide deeper
insights into customer behavior and market trends.
What is the difference between generative AI and conversational AI in banking?
Generative AI creates new content, such as financial reports or predictive
models, while conversational AI enables human-like interactions, such as
chatbots and virtual assistants for customer service. In banking, generative AI
is used for content creation and analysis, whereas conversational AI enhances
customer engagement and support.
What are the possible applications of generative AI in investment banking?
Generative AI in investment banking can be used to create predictive models for
market trends, generate investment strategies, and automate the drafting of
financial reports, enhancing decision-making and operational efficiency.
What are the use cases for AI in banking risk management?
AI in banking risk management can be used for fraud detection, credit risk
assessment, regulatory compliance monitoring, and predictive analytics to
identify potential risks and mitigate them proactively.
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