Machine learning is having a major impact on the banking sector by enabling banks to process huge volumes of data faster and more accurately to make predictions. It is being used across banking for applications like fraud detection, risk management, loan underwriting, portfolio management, and customer service. While machine learning provides benefits, banks face challenges around limited budgets, talent shortages, data privacy and security issues, and legacy systems. The continued evolution of AI is expected to further disrupt the banking industry through redefining job roles, increasing the importance of data regulations, and disrupting market structure.

1. Impact of Machine
Learning on
Banking Sector
Submitted by
Abhishek Verma

2. • Machine learning is a subset of Artificial Intelligence and broadly described as ‘learning’ based
on algorithms that can learn from data and patterns without relying on rules-based
programming
• It studies algorithms for self-learning and can process massive data faster
• Machine learning algorithms enable real-time processing of huge volume of data, deliver
precise predictions of various types such as recommending right products, customer
segmentation, detecting fraud and risks, customer retention etc.
Understanding of Machine Learning and possible application areas
Demystifying Machine Learning
Why Machine Learning is required
• Data is growing day by day, and it is
impossible to understand all of the data
with higher speed and higher accuracy
• Finding patterns in data is challenge and
costly for human brains
• The data has been very massive, the time
taken to compute would increase, and
this is where Machine Learning comes
into action, to help people with significant
data in minimum time
• The aim is to mimic human thought
processes in a computerized model
Type of Machine Learning
Supervised
learning
Text
Driven
1
Unsupervised
learning
Data
Driven
2
Reinforcement
learning
Learn from
Mistakes
3

3. Possible application areas of Machine Learning
Case in point of Machine Learning
Machine Learning are currently being used across various fields and industries, such as
Healthcare, Defence, Technology, Banking and Finance, Security and more
These areas use different applications of Supervised, Unsupervised and Reinforcement learning
Some of these areas are mentioned below

4. Impact of Machine Learning (MI) on Banking Industry
Machine Learning currently has the biggest impact on the banking industry
Banks have always maintained and operated on an extensive collection of data sets for their
customers as well as their transactions
Advancement in machine learning and artificial algorithms have now provided banks with the
ability to surpass the limitations of the human mind and thus, work in a much more efficient
manner; and has come to play an integral role in many phases of the financial ecosystem, from
approving loans, to managing assets, to assessing risks
MI in BFSI – Current Applications
• Portfolio Management
• Digital Assurance
• Fraud Detection and Risk Management
• Loan and Insurance Underwriting
MI in BFSI – Future Applications
• Customer Service
• Security 2.0 - facial or Voice recognition,
or other biometric data
• Sales / Recommendations of Financial
Products
To-do list of CIO to adopt MI wave
Machine learning are forcing CIOs to rethink
IT strategies
• Build the required strength to scale the
digital business through support for the
digital ecosystem
• Build the foundation and improve data
quality
• Involve IT professional in the design
process of Automation
• Invest in Digital Security
• Re-evaluate the key performance
indicators
• Build an exceptional customer experience

5. Shifting Competencies
Technology is altering the attributes necessary to build a successful business in BFSI
The resolution of previous trade-offs will create a new wave of transformation across the global
financial services industry
Dominant institutions in the past
were built on..
In the future, these institutions will
be built on...
Scale of assets
Economies of scale presented a cost advantage
Mass production
Physical footprint and standardized products drove
cost-effective revenue growth
Exclusivity of relationships
Ability to have direct access to many markets and
connections to investors was a critical differentiator
High switching costs
High barriers to switching providers drove
customer retention
Dependence on human ingenuity
Processes scaled through additional labour and
functional training
Scale of data
As AI drives operational efficiency, economies of
scale alone will not sustain cost advantages
Tailored experiences
AI allows the scaled distribution of highly
customized products and personalized interactions
Optimization and matching
Connections are digitized, increasing the importance
of optimizing the best fit between parties
High retention benefits
Continuously improving product performance to
offer superior customer outcomes and new
value will keep clients engaged
Value of augmented performance
The interplay of strengths across technology
and talent amplifies performance

6. Banks stand to reap billions of revenue from Machine Learning
A number of large institutes continue to invest and implement in Technology
A Capgemini surveyed results on 1500 senior executives from 750 global organisations estimate
that the financial services industry could expect to add up to USD 512 billion to global revenues
by 2020 through ‘intelligent automation’
Below are five use cases of machine learning in the banking industry
“JPMorgan Chase and their COiN
In order to automate the daily routine and
cut down the time needed to analyse the
business correspondence, Bank has
developed a proprietary ML algorithm
called Contract Intelligence or COiN.
This software program can eliminate
360,000 hours of work each year previously
done by lawyers and financial loan officers”
“NatWest has launched a fully
regulated robo-advice
proposition charging £10 for
customers seeking to invest sums
as low as £500.
The service questions users on
their financial situation, attitude
to risk and what they hope to
achieve by investing”
“Bank of America and MasterCard unveiled their
chatbots, Erica and Kai, respectively. These will allow
customers to ask questions about their accounts,
initiate transactions and receive advice via Facebook
Messenger of Amazon’s Echo tower.”
“PayPal has
successfully used
machine learning
and deployed
robust fraud
prevention models
for more than 10
years”
“Allied Irish Bank has found uses for data
science, from a scanning system that
digitises the information in customer
documentation to finding errors in tax
deductions on mortgages that can arise
due to regular changes in the rules”

7. Limiting factors in the future Banking due to Machine Learning
Key challenges and their implications
One of the main limitation of AI is the Limited Budget
Creation of smart technologies can be expensive, due to their complex nature and the need
for repair and ongoing maintenance
Talent
Financial institutions often lag in recruiting and retaining people with the knowledge, skills
and capabilities needed to create an AI-enabled workplace
Data privacy and security
Privacy and data-protection regulations are placing new limitations and requirements on the
collection, transmission and storage of personal data
Legacy system
Legacy technology infrastructure and rigid operating models are additional hurdles to
deploying AI within incumbent financial institutions
Regulation
Existing regulatory regimes often struggle to keep pace with emerging technologies, creating
roadblocks in the deployment of AI capabilities

8. Disruption in BFSI because of Machine Learning
Continued evolution of AI will enable new leaps in capabilities, causing further
disruptions in financial services
Redefinition of the ‘Future of Work’
• Current roles and responsibilities are changing the needs for talent in financial institutions,
with a significant shortage of talent emerging
• Talent transformation will be the most challenging hurdle to the implementations of AI at
financial institutions
• This challenge is compounded because many organizations are not culturally ready for the
digitization of banking or aligned on the timing and impact of AI
Data Regulations Gain Power
• Data regulations will potentially become more important than traditional financial regulations
in determining market structure
• Today, regulations of technology companies are more relaxed with regulations regarding cloud
use differing across markets
• At the same time, consumers increasingly want control over the use of their data
Disruption of Market Structure
• As AI-driven solutions continue to emerge, scale players with the lowest cost products and
smaller niche innovators that can serve unmet needs will prevail, negatively impacting regional
and mid-sized organizations the most
• This will create significant disruption of the marketplace, with providers at the extremes of size
and offerings.

9. Thank you!