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Financial Scams! Oh No! Is There Any Solution to it in the IT Industry? Uses of Artificial Intelligence and Machine Learning in Financial Scams
Financial
Scams! Oh No! Is There Any Solution to it in the IT Industry?
Top
Uses of Artificial Intelligence and Machine Learning in Financial Scams: How is
AI Transforming Fraud Prevention?
Well,
Artificial intelligence and machine learning are currently the most prevalent
technologies in business and industry. The key drivers of the digital
revolution are firms that saw opportunities in the ever-increasing amount of
data being created, gathered, and analysed.
In simple terms, AI is the concept
of creating machines that can think like humans.
Here Are Some Examples:
· Siri
-Personal Assistant
· Alexa - A
Revolutionary Product
· Tesla - The
Smart Vehicle.
· Pandora - The
Musical DNA
· Nest - The Learning Thermostat
AI is based on how humans think, learn, and work. It will surely help to build Expert Systems and Implement Human Intelligence in Machines. AI
technology will surely lead to profound societal change in the near future.
What is the Impact of Machine learning (MI) Along With AI?
The basic explanation is that a
machine or gadget might make a decision to behave in response to a circumstance
that isn't pre-programmed.
When you touch a button on an elevator dashboard, for example, the control unit has pre-programmed instructions to operate as a response to your action as an input, i.e. you pushed to number three, the elevator has particular programmed processes to follow to transport you to the third level. Machine learning is a subset of deep learning.
For example, detecting Human faces,
where you give examples for several faces and when your machine expires you are
used to a new face but still be able to recognize it as a face.
What Are The Top Uses of Machine Learning and Artificial
Intelligence in Financial Scams?
In financial frauds, AI and MI can aid in a variety of ways. Some of the applications are listed below: (Please note that I am not attempting to explain anything; rather, I am only highlighting specific scenarios in which AI may be more beneficial).
1. Stolen information from statements. Examples can be credit statements. This can help in cheating without much risk-taking.
2.
Prediction of customers’ buying behaviour for a product that the
bank offers. It can also help in identifying
customers who shall not turn back once they accept any particular product.
3. Trace through email communications between the customer and the bank to extract relevant information about the sentiments of the customer. This can help understand the nature of the conversation and the real transaction that has happened.
4. Customer service can be enhanced using bots alongside humans, thereby avoiding errors.
How is AI Transforming Fraud Prevention? What are the Applications
of machine learning in Financial Services?
Nowadays, Machine learning is
considered the key aspect of financial service and applications like calculating credit scores, approving loans,
level of risk etc.
We can classify them in the following categories:
1.
Risk: Machine
learning can be used to predict risks arising in the organisation. The risk
could be either credit risk or fraud risk. ML models can be built. The risk is
from transactions or specific customers.
2.
Sales: Predict
Assess analytics tips use machine learning extensively for identifying high
propensity customers for various activities.
3.
Financial markets: The utility
of ML for this purpose I believe is restricted because of the efficient market
hypothesis.
4.
Sentiment analysis: Machine
learning can be used to identify sentiments in data: primarily in social media
comments, news articles etc.
5.
Operational efficiency: It can be
used to improve operational efficiency. The use-case is to convert hand-written
to machine-readable data. This helps in reducing costs significantly as machine-readable
data requires a lot of paperwork.
How is AI Transforming Fraud Prevention?
As we already know AI has been for
quite a long time in the banking industry and has been steeped in tradition.
But now it’s opting for a change. These
are the key areas where banks are integrating Artificial Intelligence to scale
up the processes. The banking industry needs fraud detection the most
because online platforms of banks face the constant risks of data breaches
resulting in monetary losses.
1. It can
prevent fraud detection by using algorithms to search connections of a person’s
accounts with suspicious transactions, their credit card history and possible
loan defaults.
2. Machine learning screening solutions can calculate the potential risk level of a particular individual by running complete background checks as part of the customer due diligence.
Learn more about how AI technology
and SageMaker (cloud machine learning platform) may aid the banking industry
with financial scams and how supervised learning of these technical studies can
benefit many other industries.
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