Agile Network India - Noida Title: The AI Core: Powering the Next Generation of Banking Date: 10th May 2025 Hosted By : Aeologic Technologies

1. The AI Core: Powering the Next
Generation of Banking
Varun Jindal

2. What do you expect from your bank?
 Efficiency
 Security
 Customer satisfaction

3. How AI is impacting Core Banking:
 Gen AI in banking offers innovative solutions that enhance
these expectations efficiency, security, and customer
satisfaction by :-
 Streamlining operations
 Provide personalized and improved customer experiences
 Saving time, efforts and money
 Improving decision-making process
Note – McKinsey estimates that banks could add $1
trillion in value annually through strategic use of AI.

4. First step for India - AAs (Account Aggregator)
 Account Aggregator (AA) system
 Launched in 2021 while keeping user privacy and data
intact.
 This system enables data collection with user consent, to
be shared among financial institutions.
 AAs enables a financial institution to make a more informed,
expansive data-driven decision.

5. AAs (Account Aggregator)
 FIPs (Financial Information Provider) - Banks, mutual fund houses,
GST platform and the national income tax portal etc.
 FIUs (Financial Information User) - Banks, NBFCs, pension funds,
insurance agencies, wealth managers and asset management
agencies etc.
 AAs Collects information from FIPs and share them
with FIUs

6. Implementing AIs with AA (Account
Aggregate) will help
Banks will be able to reach the under-served and
under-banked populations in India specially Tier
2 and Tier 3 cities customer

7. Existing Structure
 Automated customer service and chatbots
By statistical modeling, machine learning (ML), and deep learning
 Risk assessment and fraud detection with AI
 AI-powered investment and wealth management solutions:
By Analyzing market data
 Loan and credit analysis:
By Analyzing behavior and patterns of customers
 Process automation
.
 Regulatory compliance:
By Using AI and machine learning to read new compliance
requirements

8. A case study by RBI
 “How Indian Banks are Adopting Artificial Intelligence?”
(Published in 2024) RBI discovered -
 A higher AI adoption rate among banks with larger asset
sizes.
 A major challenge to Small banks in terms of their survival
and product designing in comparison to larger banks.
 The Capital To Risk weighted Asset Ratio (CRAR), that
reflects bank’s capital adequacy and its financial health, has
a positive relationship with the AI score.

9. Gen AI revenue forecast in Banking
Global Market Report 2025

10. Recent AI implementations in Banks
BANK OF AMERICA (Erica) – to Build customer profile and
recommend products accordingly
STANDARD CHARTERED – Stacy (offering services like
account inquiries, transaction history, and credit card
rewards)
HSBC BANK – Amy
ROYAL BANK OF SCOTLAND – Cora
HDFC BANK - Eva

11. EVA with HDFC BANK
HDFC BANK developed smart chatbot EVA to
leverage Artificial Intelligence & Machine Learning.
(EVA - Electronic Virtual Assistant Banking chatbot)
It helps checking on a loan status, facilitating
payments and getting instant answers to FAQs.
Within milliseconds understands the user query &
fetches the relevant information from thousands of
possible sources using NLP (Natural Language
Processing)
This EVA banking chatbot has Capability to answer 5
million queries with 85% accuracy for around 1 million
customers.

12. Main Technologies of Gen AI in BFSI
 Natural language processing
 Deep learning, Reinforcement learning
 Generative adversarial networks
 Computer vision
 Predictive analytics

13. NLP(Natural Language Processing)
A rule-based modeling of human language, with statistical
modeling, machine learning (ML), and deep learning.
This combination allows computers to analyze and process
text or voice data, by grasping their full meaning, including
the speaker’s or writer’s intentions and emotions.

14. The Future of AI in banking
 Advanced personalization:
To offer hyper-personalized services tailored to
individual customers' needs and preferences.
 Enhanced security measures:
To detect and respond to fraudulent activities in real-
time, ensuring the protection of customers' assets and
sensitive information.
 Automate compliance processes:
To monitor transactions and flag potential violations,
reducing the risk of non-compliance and streamlining
regulatory reporting.
 Improved Operation Efficiency:
Process automation is increasing operational efficiency.

15. AI in Banking – New and faster sales opportunities
How to reach the correct client and to improve client experience
 With the power of both predictive and generative AI, banks can
understand the best channel to reach the client and to fully
understand customer needs while improving their experience.
 Predictive AI can surface relevant insights to deepen existing
relationships with new products and services or capture new
clients for the bank.
 Generative AI can integrate data from third parties as well as
internal sources to make suggestions in the flow of work, which
increases the accuracy and relevance of those recommendations
that results into more informed decisions

16. AI in banking - Marketing personalization
Marketing personalization - this tools help marketers quickly
build the most relevant offers or promotions, then test and
learn from each, to further refine segmentation.
How - The marketers can use GEN AI-powered, prebuilt email
templates/ text messages/ watsapp categories to share an
offer with the targeted customer.
Over time, the messaging gets refined as the AI engine learns
how customers respond to the content. The net result: Offers
become super-personalized and conversion rates improve.

17. AI in Banking – Managing Employees
and customer experience
GEN AI can improve the training experience and the day-to-
day workflow enables Bank employees or agents service
standards better and more pleasant for the customer.
 How - By populating content for known answers based on
the actual language the customer uses to describe a
problem. This empowers Bank to make smart decisions,
and that’s important in cases that require judgment calls
For Example - whether it’s OK to reverse a charge for an
unhappy customer.

18. Challenges of AI in banking
 Fairness and transparency in AI algorithms
 Unfair outcomes - loan approvals and risk
assessments
 To comply with regulations and having a
collaboration with regulatory bodies
 Ensuring data privacy, managing
cybersecurity risks, and adhering to ethical
standards is a challenge

19. Steps to be taken by bank now
 Banks has upgrade data collection and management
processes.
 Banks has to evaluate the quality and quantity of
their data
 Deploying AI in banking requires further unique
data management, with varying access rights for
different functions.
 Leveraging solutions that have built-in data integrity
like ethical guardrails to meet compliance rules.