The words you are searching are inside this book. To get more targeted content, please make full-text search by clicking here.
Discover the best professional documents and content resources in AnyFlip Document Base.
Published by Enhelion, 2020-04-26 06:18:17





Artificial intelligence penetrates into all fields of activity. According
to Markets&Markets research, the global AI in FinTech market will grow to 7,305
million by 2025 in comparison to 1,337 million in 2017.1 Today, we have started
using AI in our financial operations and sometimes even don't pay attention to it.
Apart from some new hardware and software created to enhance a financial field,
artificial intelligence makes significant contribution to the development of FinTech

and provides solutions to main challenges in this field.

Fintech is an industry aiming to disrupt financial services and with Artificial
Intelligence as a partner, it just might change society’s perspective. Until recently,
application of robotics was unheard of in banking and was considered for application
primarily in the manufacturing & medical sectors. With the use of Intelligent Robotic
Assistant (IRA), robotics are being brought into the mainstream of customer service and
support. IRA is designed to assist branch staff in large branches, which have high
footfalls, by guiding customers to carry out their banking transactions. AI is becoming an
integral part of the banking system, functions, processes and customer interactions. Both
Robotics and AI will help banks manage both internal and external customers much more
effectively and help reduce operational costs exponentially in the future. The potential of
AI and Robotics based solutions is enormous and will revolutionize the way people do

There are three primary ways in which artificial intelligence will transform the
banking and financial industry:

1. AI technology companies such as Google and Amazon will add financial services
skills to their smart home assistants, then, leveraging this data+interface via
relationships with traditional banking providers.

1 Markets & Markets, Artificial Intelligence Market by Offering, Technology, End-User Industry and
Geography – Global Forecast to 2025 <available at:

2. Technology and finance firms merge/collaborate to build full psychographic
profiles of consumers across social, commercial, personal and financial data (e.g.,
like Tencent coupling with Ant Financial in China).

3. The crypto community builds decentralized, autonomous organizations using
open source components with the goal of shifting power back to consumers.

AI-enabled devices are already using vision and sound to gather information even
more accurately than humans, and the software continues to get more human-like.

Every application of AI on the consumer level will require some level of payment and
that’s where Fintech comes in. Such a relationship between the two trends will
become key for the success of digitalization in the future. In addition, AI has the
potential to eliminate human error in banking procedures, allow banks to truly
understand customer demands, make credit cards extinct, and influence the attraction
of the unbanked to financial services. It will bring upon a digital revolution to banking
that will transform the processes into ones that fit consumers’ lives. 2 FinTech
prioritizes financial inclusivity. To achieve this, real time plays an important role in
FinTech’s ease of adoption as individuals with a smartphone gain access to quick,
personalized and customized financial services. As AI steps in to disrupt the who,
what, when and how of finance, instantaneous decision-making and credit scoring
will improve the availability of services in a real time basis.3

Artificial Intelligence is a catalyst for financial institutions to innovate and provide
products and services that are technologically progressive. In fact, a large chunk of
investment being funnelled into FinTech start-ups has been towards the development
of AI-centric innovations. AI provides unlimited potential for corporations to improve
results by applying methods derived from aspects of human intelligence at a scale a
human can only dream of. AI can particularly be used to process huge amounts of
information about customers and aid FinTech across a wide spectrum of functions,

2 Christine Duhaime, Artificial Intelligence in FinTech, Digital Finance Institute <available at:>.
3 Christine Duhaime, Artificial Intelligence in FinTech, Digital Finance Institute <available at:>.

resulting in not just decreased costs, but, also enhanced customer experiences and
Consumers remain wary of AI applications, particularly in banking. In all honesty,
consumers aren’t even sure what AI is, so perhaps they are simply afraid of the
unknown. Sadly, only 44% of consumers in a survey from SAS said they could
explain AI to someone else. And, they aren’t convinced that personal data used in AI
situations is being protected, with only 35% saying they were confident.5 An even
more serious problem seems to be the fact that several customers cannot really
pinpoint what AI is being used for in financial services, as per the figures provided by
a market research.6

Consumers are fine with AI in healthcare settings, but are notably less comfortable
with banks and credit unions using AI. More consumers say they trust healthcare

4Chris Middleton, FinTech: Intelligent Automation could add $512 billion to Finance Sector, Internet
of Business <available at:
5 SAS, Artificial Intelligence Survey <available at:
6 Statista and Edelweiss Research .

providers to use AI to perform surgery or suggest medical treatment than they would
trust banking providers using AI to provide financial guidance. AI in retail also gets
the nod of approval from consumers: almost half say they are comfortable with
retailers using computers and drones to fill and deliver orders.

The only area that consumers are comfortable with banks and credit unions using AI
is in monitoring threats such as fraud, with 59% of consumers saying that using AI
was okay. The least popular use of AI was for analyzing consumer credit history to
make a credit card recommendation.7

Consumer trepidation with AI in financial services could be attributed to a lack of
understanding in how AI could improve consumer experiences or make their financial
lives more convenient and even, healthier. Just like banks and credit unions face a
steep AI learning curve, the same is true for consumers.

8.1. Accurate Decision Making

Data-driven management decisions at lower cost lead to a different style of
management, wherein, insurance leaders and future banking agents will seek answers
from machines rather than human experts. In the last few years, several businesses
globally are now switching to an AI-led, algorithm-augmented style of decision
making bolstered by huge computing power and an ever-growing data
storage/analytic prowess.8

AI have been increasingly adopted by Hedge Funds for their decision making
capabilities. Hedge funds are private investment funds that are focused only on
professional rich investors. And, now hedge funds are interested in using AI systems
that will be able to process large volumes of data and improve the quality of analysis
of investments. Many hedge funds have already started automating part of their
investments using computing models, but their results were not very good. So AI in

7 SAS, Artificial Intelligence Survey <available at:
8Sameer Dhanrajani, Reimagining Enterprise Decision-Making With Artificial Intelligence, Forbes
(Oct 15, 2018) <available at:

FinTech was considered to be one of the most appropriate ways to improve
automation process.9

Ideally, AI can help create a hedge fund that performs all stock exchange operations
without human intervention. Every day AI algorithms make its own predictions about
market upon a complete analysis of market prices and so on. And then AI system can
make accurate decisions what is the better option from all possible for today. So,
investors can become our customers as well, if they want to improve investment
process and increase a profit.

8.2. Automated Customer Support

AI can also be used to improve customer experience in terms of communication, by
contributing to virtual assistance programs, such as ‘Chatbots’.10 Chatbots and AI
interfaces like Cleo, Eno, and the Wells Fargo Bot interact with customers and answer
queries, offering massive potential to cut front office and helpline staffing costs. The
London-based financial-sector research firm produced a report which predicts that
the finance sector can leverage AI technology to cut 22% of operating costs - totalling
a staggering $1 trillion.11

Major advances are being made using multiple machine learning techniques including
speech recognition and natural language processing systems that turn spoken words
into data. From this “raw material”, AI-powered platforms can extract meaning,
detect emotion, interpret context, and ultimately frame an appropriate response.12

These virtual assistance programs have the capability to deliver human-like service or
advice at a lower cost. These automated programmes use NLP to interact with clients
in natural language (by text or voice), and use machine learning algorithms to
improve over time. Chatbots are being introduced by a range of financial services
firms, often in their mobile apps or social media. While, many are still in the trial

9 CleveRoad,AI in FinTech: How Artificial Intelligence Can Change your Financial Business,
<available at:
10 Maruti TechLabs, Chatbots as your Personal Finance Assistant <available at:>.
11The Financial Brand, Artificial Intelligence and the Banking Industry’s $1 Trillion Opportunity
<available at:>.
12 The Financial Brand, Artificial Intelligence and the Banking Industry’s $1 Trillion Opportunity
<available at:>.

phase, there is potential for growth as chatbots gain increasing usage, especially,
among the younger generations, and to become more sophisticated.

The key is- using NLP, by utilizing deep learning algorithms to understand language
and generate responses in a more natural way. Financial companies need instant on-
demand access to internal data to keep up with the competition. Natural language
search enables organizations to perform such a search in a matter of seconds. NLP
translates a human language into a SQL request and has results delivered in
convenient visual form. In modern markets, when banks operate 24/7 in different time
zones, having relevant information at hand can save billions of dollars and help make
well informed strategic decisions.13 Swedbank, which has over a half of its customers
already using digital banking, is using the Nina chatbot with NLP to try and fully
resolve 2 million transactional calls14 to its contact center each year.15

The current generation of chatbots in use by financial services firms is simple,
generally providing balance information or alerts to. Some chatbots even have the
ability to gauge the customer’s profile and suggest the best possible plan accordingly,
in terms of suitability.16

The most mature use cases are of chatbots in the front office, antifraud and risk and
KYC/AML in the middle office, and credit underwriting in the back office. Financial
institutions can use AI to power conversational interfaces that integrate financial data
and account actions with algorithm-powered automatic “agents” that can hold life-like
conversations with consumers.

To build a chatbot or voice channel, developers need to select from (1) competing
open-source frameworks and private platforms that power machine learning and
associated NLP, and (2) the end points that touch consumers, from Slack, Skype,
Messenger, Telegram and several large Asian tech players – Chatbot platforms can be

13 HackerNoon, How AI is Used in FinTech <available at:
14 SwedBank, SwedBank’s Virtual Assistant Creates A Personalised Digital Customer Service,
<available at:>.
15 Net Guru, AI and Machine Learning in FinTech: 5 Areas Which Artificial Intelligence will change
for good <available at:
16 Naveen Joshi, How AI and FinTech can Work Together, Allerin Blog <available at:>.

consumer facing as direct distribution channels, as well as private label platforms for
banks and financial institutions to more cheaply serve their customers

8.3. Fraud Detections and Claims Management

Machine Learning can also act as an anti-fraud mechanism for FinTech companies.
As the volume of transactions via FinTech grows, fraud identification and claim
management would start to become a real hassle for companies in the sector. In
addition, it is of vital importance to companies since several legislations and
provisions relating to fraud state that the legal liability of fraud is often on the
provider of financial services. AI-driven support could remove excess human
intervention and report the claim, capture damage, update the system and
communicate with the customer all by itself.17

There are several ways of using Machine Learning to combat fraud in the financial

1. Machine Learning Systems can help in learning the
behaviour of a financial institution’s customers.
Companies should integrate systems based on Know Your
Customer (KYC) behavior. That is, it is necessary to analyze
each customer in detail, models don't always orient on the
history of all every transaction, but on actions of a specific
customer, learning his or her typical features and actions. If
the customer makes a transaction that is far from his habitual
behavior pattern, the system will notify you about it, and it
means you need to verify why it has happened immediately.
It helps fight fraud since the system remembers the
behavioral and buying patterns of customers and responds to


17LexisNexis, 2017 Future of Claims Study: Is The Future of Claims Touchless <available at:>.
18 Altex Soft, How Machine Learning Systems Help Reveal Scams in FinTech and Healthcare,
<available at:

Thus, all the customer's actions can be divided into the
categories of ordinary and suspicious. The advantage of this
method lies in the fact that models stop depending on data
received in the process of machine learning. Data can collect
unnecessary information that has nothing in common with the
specific customer, or there might not be enough data.

2. The second method represents the detection of suspicious
financial manipulations through the analysis of all available
data. Advantages of this approach are a higher quality and
more accurate models, since it is learning a large array of
information and it is using deep learning for fraud detection.

But, machines can still come across false positives
transactions that are not breaking the law, but the algorithm
detects it as illegal. So, today machine learning algorithms
cannot exist completely autonomously. Specialists need to
verify all operations that the algorithm detected as fraudulent,
and fix the problem if it is a false alarm.

3. Analysis of social media is becoming more popular since it
helps detects many dangerous and maleficent operations. And
all accounts can be visualized as a social network where a
payment transaction is equal to a personal message. The goal
of the algorithm is to detect where money is vanishing from
in a suspicious manner - one of the most widely spread frauds
in banking sector, for example.

4. Automation of routine processes – Apart from the detection
of fraudulent schemes, machine learning can help automate
some routine processes of a financial job like the creation and
preparation of reports, mailing notifications, and improving
and accelerating the accounting process. As a result,
efficiency grows, and labor intensity, as well as operational
costs, are decreasing.

Machine learning cannot cover all financial areas in full, but
it substantially simplifies the work of employees that can pay
more attention to investigation and prevention of other illegal
schemes. And, the more false positives signals specialists will
see and mark as false, the smarter machine learning-based
system becomes. And, one day it will be able to detect all
illegal actions with 100% accuracy.

5. Controlling User ID Information: Due to newly developed
solutions, companies can analyze the history of transactions
for building a model that can detect fraudulent actions. And,
machine learning technology is also used by fintech
companies for the biometric authentication of the user.
Before signing into the account, the user takes a selfie using
an ordinary camera. But, inside this camera, machine learning
software analyzes the ‘selfie’ and identifies the user by the
map of veins in the whites of the eyes and other individual
particularities of eyes.
Fraudsters can counterfeit user data, speak on behalf of the
user, but, they cannot create a precise map of the user's eye.
So, machine learning moves in on every front.19

Financial Institutions and FinTechs need to analyze which of these approaches they
would prefer to incorporate into their fraud detection mechanisms in order to combat
fraudulent claims and verify authenticity. They merely need to control it and guide
AI in the right way, so that they may be able to secure their finances.

Compliance analysis is shifting from examining a selected sample of all transactions
in a batch process (e.g., 5% of a month) to a continuous AI evaluation of every single
transaction in real time. This is particularly true for e-commerce and payments
systems, which must make credit decisions in near real-time on millions of

19 CleveRoad, How Can Machine Learning Protect Your FinTech App From Fraudulent Attacks,
<available at:

AI can learn and monitor a user’s behavioural patterns to identify rarity and warning
signs of fraud attempts and incidences based on already collected evidence and data
analysis. The claim management can also be made more efficient by training the AI to
recognize fraudulent claims.

Visual identification using neural networks help P2P lending business owners and
bank owners verify the identity of the person who wants to take a loan and verify his
or her ID document to make sure that this is the right person who applies for a loan.
Moreover, AI can also be used now in credit admin software that is working with loan
documentation and automate the whole process of verification. Application of
artificial intelligence in financial services makes it possible to modify all financial
procedures and make them more secure.

8.4. Insurance Management

AI and Machine Learning can help make the underwriting and data utilization aspects
of insurance management automated. Automated agents can assist the user online, in
determining insurance requirements. Insurance usually comes into the picture after the
loss has occurred. Automatic underwriting can extremely speed up the process and
often deliver test results by linking several relevant data sets, even external ones that
are not present in the medical records.

In insuretech, most of the operations facilitated by AI use are customer-related:
personalized telematic devices that track driving and fitness trackers reporting to
insurance companies, on exercise and health playing an important part in client risk
profiling used by insurance companies. AI tools then automatically select insurance
products suitable for each risk profile and offer them to customers via virtual

AI should be used first, in insurance of business types related to large data volumes
like real estate and motor transport. Artificial intelligence can be used here for the
assessment of damage level after a car accident or for monitoring of house condition.

20 Ben Deda, What is InsurTech and How Can you Harness its Disruptive Powers?, VertaFore,
<available at:

Artificial intelligence has a great potential to change insurance industry, improve
products and simplify services. Machine learning will be able to replace human and
do this job easily and fast. Along with Big Data and IoT, artificial intelligence will
help consumers and insurers to decrease the number of insurance claims and turn
insurance into a preventive service instead of an indemnity service. Insurance
business definitely requires artificial intelligence to enhance the business and provide
people with a first-rate service. This is the age of artificial intelligence in FinTech.

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.21

Insurance underwriting can use machine learning on applicant data to price policies.
Additionally, insurance companies can use machine vision to assess claims, such as
accident damage to a car, flooding damage to a house, or yield damage to crops.22

Instead of paying for the treatments that are costly for insurance, it’s better to detect
the risks and diseases to prevent them. One can, hence, employ the data that was used

21 Swathi Young, Use of Artificial Intelligence and Machine Learning in Financial Services, Medium –
Data Driven Investor Blog <available at:
22 Autonomous, Augmented Finance and Machine Intelligence, <available at:>.

before to access the risks, to then lower the probability of damages happening to the
insured and also for the insurer.23

8.5. Automated Virtual Financial Assistance

Automated financial assistants and planners often assist users in making key financial
decisions. They do so by tracking events, stocks and bond price trends tailored to the
user’s financial goals and personal portfolio, which can help in making
recommendations regarding bonds and stocks to buy or sell. There have been
groundbreaking advances in this regard, like AI capable of recognizing and adapting
to the emotions displayed by the interlocutor24; Certain banks25 have already jumped
on the AI-financial assistant bandwagon, such as Bank of America and Wells Fargo.26
Bank of America has, in fact, announced that it is aggressively rolling out Erica, its
virtual assistant, to all of its 25 million mobile banking consumers. Using voice
commands, texts or touch, Bank of America customers can instruct Erica to give
account balances, transfer money between accounts, send money with Zelle, and
schedule meetings with real representatives at financial centers.27

Automated Virtual Financial Assistants 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

23 Maruti TechLabs, How Can Artificial Intelligence Help FinTech Companies <available at:>.
24 SoftBank
Robotics’ Humanoid Robot ‘Pepper’ <available at:>.
25Commonwealth Bank Launches Chatbot Named CEBA, Commonwealth Bank AU Media Release

<available at:

26Bank of America, Introducing Erica Insights: Bank of America’s AI-Driven Virtual Financial

Assistant Just Got Smarter, BoA Press Release <available at:

bank-americas-ai-driven-virtual>; Wells Fargo is Testing a Chatbot in Facebook Messenger <available

27 The Financial Brand, Artificial Intelligence and the Banking Industry’s $1 Trillion Opportunity

<available at:>.

needs. An example of this is Wealthfront, that helps in automating investments and
offers all-in-one automated solution.28

8.6. Predictive Analysis in Financial Services

Predictive Analysis is often the foundation on which decisions affecting strategy and
optimization are made. It can often provide a company with the requisite edge by
improving business operations, internal processes and outperforming competitors.
There are several types of predictive analytics: customer analytics, workforce
analytics, and operational analytics, which includes process optimization as well as
predictive surveillance tools for compliance or internal audit processes. Customer
Analytics model customer behaviour encompassing customer attrition, customer
loyalty, customer experiences and marketing analytics. This type of analytic work
caters to strategic and business developmental fields of companies.29

Firms generally have an infinite amount of data sitting on their systems and servers.
They can use these unstructured assets and data – by programming AI to sift through
these data and undertake an analysis of it, they can reveal insights they never knew
existed. In addition, using predictive analysis and modelling tools, they can predict
(with some accuracy) as to what the eventual outcome of a particular thing would
ideally be.30

Due to the massive amount of data that is required to map patterns and hypothesize
insights, predictive analysis can be done with the aid of AI.31 For example, it can be
used to predict a customer’s product preferences, prioritization of certain prospects or
leads depending on the profile of the end-user, predict the satisfaction level of a

28 WealthFront Advisory, <available at:>.
29 Protiviti, Innovation in Predictive Analytics <available at:
30 Protiviti, Innovation in Predictive Analytics <available at:
31Reuben Jackson, AI Driven Predictive Analytics: New Opportunities for Financial Institutions,
ReadWrite<available at:

customer depending on the time taken to resolve a ticket and to gauge the possibility
of a particular illness (in medical diagnosis).32

A predictive modeling process is where data scientists, statisticians and quantitative
analysts use data mining tools to build predictive models that produce fraud
propensity scores. As data is entered and updated, claims are automatically scored for
their likelihood to be fraudulent and made available for review. Predictive modeling
tends to be more accurate than other fraud detection methods. Information can be
collected and cross-referenced from a variety of data sources. This diversity of
resources provides a better balance of data than the more labor-intensive business
rules flagging system.33

Predictive analytics is one of the most important big data trends affecting FinTech.
Established financial companies like Payoneer and PayPal have already started using
this new technology to improve their business models.34 There are a number of
applications for predictive analytics in these verticals. One of the biggest uses of
predictive analytics is fraud prevention. Cyber fraud is a growing problem that is
threatening organizations all over the world. Due to the massive volume in funds that
are transferred every day, FinTech companies are some of the most attractive targets
of cyber criminals.35

A number of new predictive analytics algorithms have helped these firms identify
potential fraudsters. Their fraud scoring algorithms use a number of variables, such as
the nature of IP addresses, association with suspicious email addresses, region of the
user and the believability of the names on their accounts.36

32 Maruti TechLabs, How Machine Learning Can Boost Your Predictive Analytics<available at:>.
33 SAS, Simplifying Fraud Analytics: 10 Steps to Detect and Prevent Insurance Fraud <available at:
34 Protiviti, Innovation in Predictive Analytics <available at:
35 Naveen Joshi, How AI and FinTech can Work Together, Allerin <available at:>.
36 SAS, Simplifying Fraud Analytics: 10 Steps to Detect and Prevent Insurance Fraud <available at:
105573.pdf> .

Fintech firms are increasingly escalating the pace of revolution with the help of
cutting-edge technologies. They are now looking to combine two incredible
technologies to become a differentiator in the competitive market. Robotic process
automation (RPA) and AI have become a disruptive force in the fintech sector.
Augmenting AI to a rule-based robotic process automation system gives rise to
another tool that not only automates tasks, but also possesses decision-making
capabilities. Yes, you guessed it right. It is ‘Intelligent Process Automation’. This
newly born tool is expected to add 512 billion dollars to the finance sector, as
revealed in the report from Capgemini Digital Transformation Institute.37

Robotic process automation tools are programmed to carry out only specific tasks,
and so they fall short to process unstructured data. But, now with intelligent process
automation (IPA), this problem will be solved completely. Intelligent process
automation can analyze structured, semi-structured, and unstructured data, learn with
time how to undertake different tasks, and take decisions on its own. For example,
IPA can comprehend the incoming mails using NLP capabilities and take appropriate
action. After reading a mail, it will understand whether the mail is from a potential
customer or just spam. If the mail is from a potential customer, it will automatically
compose mails for the concerned executives, telling them to contact the lead. Else, it
will just send a greet message to the sender. IPA can also find leads who might be
interested in investing. It can examine their portfolios, gauge their financial status,
and send them best-suited plans through mails. Apart from that, IPA can actually
carry out a lot many things such as underwriting, report generation, claim processing,
and so much more, with high processing speeds and no errors.

In today’s high-paced technology-driven world, fintechs are surpassing their
competitors by following the new-age trends that have arisen because of advanced
technologies. While these firms have successfully managed to satisfy today’s new
generation and their needs, their demands for more convenience and ease will never
end. The groundbreaking technology comes as a life-saver for these firms. Even
though leveraging AI will take immense efforts and might eat a lot of money, but it

37 Capgemini, World FinTech Report 2018 <available at:

will stand poised to revolutionize the financial services, operate smartly, and make
better decisions with time.38
Fintech companies also use predictive analytics to conduct risk analysis of potential
borrowers. This has proven to be a great way to reduce the risk profile of their
networks.39 Automation will have a huge impact on society as a whole – and it’ll be a
big factor to watch in fintech especially. This could create tension, of course, and
there needs to be careful thought about how to re-skill the workforce to find roles for
people that fit with a newer, more digitally-led era.

38 Naveen Joshi, How AI and FinTech can Work Together, Allerin <available at:>.
39 Smart Data Collective, Ways in Which FinTech Companies Use Big Data To Beat Banks <available

Click to View FlipBook Version