The use of Artificial Intelligence and Machine learning is increasingly adopted in multiple industries. Question is, does a regulated industry like Finance adopt AI/ML? the answer is a huge YES! Here we take a look at 6 different use cases: * Chatbots * RoboAdvisors * Risk scoring * Fraud Detection * Insurance claims * Underwriting * regulatory compliance

1. 1
Page
Presentation 2021 By Swathi Young
May 2021
Presentation 2021
Swathi Young
CTO, Integrity Management Services, Inc.
Forbes Technology Council Member
Women in AI, Washington DC Ambassador

2. 2
Page
Presentation 2021 By Swathi Young
These days, there’s so much noise about AI. And in the midst of all these, the importance
of AI and how it has become a key feature in most industries can easily get buried. Yes, it
makes appearances in the news almost every other day. But what are its uses in an
industry as sensitive as the financial sector?
It’s unsafe to assume everyone really knows what AI is. So a quick definition is necessary
at this point.
There are many definitions you will find online. One particular one describes Artificial
Intelligence as a branch of computer science that aims to create intelligent machines. In
other words, AI is software/algorithms that can perform tasks that are smart and perform
tasks that require little human intervention. Machine learning is a subset of AI and is the
most common way of solving problems using AI. Machine learning uses vast quantities of
data and creates algorithms that can be used for prediction, optimization or
categorization challenges.

3. 3
Page
Presentation 2021 By Swathi Young
2018 saw an increase in practical application of
AI in various industries and one of them is the
financial sector. There are a host of applications —
market and customer analysis, credit scoring, usage-based
insurance, data-driven trading, fraud detection and beyond.
Various sub-sectors like banks, insurance companies, credit
card and payment processing companies, asset and wealth
management firms, lenders etc. led major investments in AI
in the financial services that accounted to nearly $9 Billion in
2018 alone and is expected to grow at a CAGR of
approximately 17% over the next three years.

4. 4
Page
Presentation 2021 By Swathi Young
Drivers of adoption of AI and machine learning in financial services:
There are a wide range of factors that have contributed to the growing use of AI and
machine learning in financial sector. Some of them are:
Faster Processor Speeds Lower Hardware Costs Easy Access To
Computing
(On The Cloud)
This means an increase in the availability of infrastructure both to analyze data as well to
extract insights and develop modelling capabilities. The other factors are availability of AI
and machine learning tools. Yet another factor is the proliferation of data in digital formats
from multiple sources such as online search trends, viewership patterns and social media
that contain financial information about markets and consumers.

5. 5
Page
Presentation 2021 By Swathi Young
How is AI/Machine learning used in the
financial sector?
Some of the scenarios in which AI/machine learning could
become valuable in the context of financial services is to
automate routine processes like application/case intake and
answering customer calls using chatbots. Other opportunities
for improvement include risk management analysis,
productivity improvements, and enhanced decision-making
and solve significantly complex problems. Most financial
institutions also find it increasingly necessary to keep up with
competitor adoption of AI and machine learning as well as to
increase valuation as a Fintech startup.
(Uses of AI in Financial services, Source : CBInsights)

6. 6
Page
Presentation 2021 By Swathi Young
Here are some in-depth use cases:
1. Fraud Detection
Financial Institutions have a responsibility to protect their customers
and with the help of machine learning, this is made possible. Given
the history of a customer’s financial transactions, amounts, location,
time of transaction, merchant/vendor they payment is being made to
constitutes vast amounts of historical data. Machine learning
algorithms can how classify if the current transaction is fraudulent or
not based on the historical transactions that it is able to analyse and
classify. A good example is FICO’s cognitive fraud analytics.

7. 7
Page
Presentation 2021 By Swathi Young
2. Personalized Customer Service
Financial sector is always challenged with personalizing service to their customers. It was
almost impossible for a bank, with over 10 million customers, to offer customized services to
each of them.
But with the introduction of AI, it is now possible.
Financial institutions now use AI to help provide their clients with a personalized experience.
These days, consumer financial services provided is customized with the help of machine
learning algorithms that study a client’s financial data, financial habits, spending style, savings,
and predict with accuracy a product that customers would like. As a result, customers can now
pick what is suitable for them. This is usually done via chatbots that are voice-enabled and are
powered by machine learning programs that use NLP (natural language processing is a sub-
domain of AI where a program can communicate with customers using a language that they
understand). For eg, Wells Fargo’s chatbot delivers “information in the moment”
Source: Wells Fargo

8. 8
Page
Presentation 2021 By Swathi Young
3. Underwriting
Whether it is underwriting in credit card/banking organizations or actuary
services in insurance agencies, the underlying process is to understand a
potential customer’s history of financial transactions, credit scores and
payment transactions to determine lending decision as well as to reduce
risk.
Machine learning can expedite this process by considering details from
unstructured data such as social media analysis call phone usage and
prompt payments of utilities. In addition, it can also determine credit-
worthiness for a person who has a thin –credit file by looking up alternate
data sources (My friend who recently moved from Singapore is an example
of this. The credit score cannot be transferred to the US but she is a low-risk
customer). For example, Upstart provides financial institutions a unique
underwriting engine using machine learning.

9. 9
Page
Presentation 2021 By Swathi Young
4. Managing Insurance Policies
Adoption of AI and machine learning in insurance has led to insurance-tech organizations
who use machine learning in their underwriting process, sorting through vast data sets to
identify cases that pose higher risk, potentially reducing claims and improving profitability.
They are also using machine learning to improve the pricing or marketing of insurance
products by incorporating real-time, highly unstructured data, such as online shopping
behavior or IoT telemetrics (sensors in connected devices, such as car odometers).
They also use machine learning in claims processing to determine the damage and cost of
vehicles due to accidents. They are increasing researching interconnecting IoT (sensor
information using Internet of things technology) and AI/machine learning to determine
predicting incidents before they occur such as chemical spills, car accidents or even flooding
of basements. One such example is daisy intelligence that is using machine learning to help
insurance companies to deliver real-time operational recommendations

10. 10
Page
Presentation 2021 By Swathi Young
5. Regulatory Compliance
Regulated institutions are increasingly using AI and machine learning for regulatory
compliance. RegTech is a subset of FinTech that focuses on facilitating regulatory
compliance more efficiently and effectively than current industry standards.
The total RegTech market is expected to reach $6.45 billion by 2020. RegTech uses
machine learning to meet complex regulatory standards thereby reducing risk and
enhancing compliance. Knowing the identity of customers (‘know your customer’ or
KYC) is a common first step in most financial institutions. This process is expensive,
labor-intense, and highly duplicated. Machine learning is used to perform identity and
background checks beforehand. It is used in two ways: (1) algorithms evaluate images
to identify if documents match one another, and (2) calculate risk and flag those that
require additional scrutiny. Machine learning programs also publicly available data
and other data sources, such as police registers/ judicial cases and social media
accounts.

11. 11
Page
Presentation 2021 By Swathi Young
6. Robo-Advisors
Robo-advisors are increasingly providing automated investment advice as
well as offer brokerage and investing services. They also provide services
around other investments like retail investors. Some of them even provide
zero commission based trading. This allows customers of any income-level
access to management of their personal finances. Robo-advisors use
machine-learning algorithms to determine and manage ETFs (exchange-
traded funds). This helps customers with a portfolio of diversified, low-risk
investments according to their needs. An example of this is Weathfront, that
helps in automating investments and offers all-in-one automated solution.

12. 12
Page
Presentation 2021 By Swathi Young
Conclusion
AI is the new cool in the financial sector. The observation
of key trends and use cases noted above provide ample evidence that
Artificial intelligence has now become an integral part of the financial
services market and it will only gain momentum in 2019 and the years
to come.