Data science is essential to the economy and the financial
institutions that underpin it, from credit card transactions to stock pricing.
The vast volume of data that organisations need to analyse might be one of the
major issues with data. Fortunately, machine learning in financial services is enabling businesses to analyse data and
put insights to use even more quickly than people can.
Financial services are already benefiting from artificial
intelligence and machine learning, and these technologies are developing
quickly. In the next five to ten years, the employment positions in the sector
are anticipated to change as a result of new analytical techniques, such as
artificial intelligence and machine learning, according to a CFA Institute
poll.
Financial service businesses may use machine learning to
increase productivity, reduce costs, and regulate expenses. Discover the
benefits of machine learning for financial organisations like yours, the
difficulties of implementation, and 10 methods your company may get going.
What's the Need for Machine Learning in Finance?
Due to its real-time operations and massive data production,
the financial services sector has shown a special interest in machine learning.
Financial institutions want a mechanism to swiftly analyse data with little
human participation.
For efficiently sorting and filtering through a vast volume
of data, conventional techniques like manual analysis or rules-based systems
fall short. Utilising machine learning enables you to make use of potent
algorithms that can analyse data from several data sources, identify trends,
and develop systems that can forecast future events. Financial institutions who
don't use machine learning will find it difficult to stay up with those that do
as this technology develops.
Using machine learning in the banking sector is about more
than simply productivity and cost savings. It also involves giving clients
value, such as individualised advice that aid in their goal-achieving.
Effective MLOps techniques and machine learning may help you better understand
your customer's demands and how to enhance their experiences.
Challenges in Financial Services Using Machine Learning
Financial services and machine learning both rely on
enormous collections of extremely complicated data. It's challenging to manage
such complexity. Here are three typical difficulties with machine learning.
Data Bias
Bias is a problem for any contemporary predictive insight
using data. This also applies to artificial intelligence and machine learning
in the financial services industry. The algorithm may be to blame for this
prejudice, but even the finest algorithms are only as good as the data they are
provided. For instance, if a model is trained using customer information from
particular ZIP codes, it may unintentionally bias against residents of other
locations, leading to discrepancies in access to credit, financial aid, or
other crucial services.
Human bias is another way that data bias makes its way into
machine learning algorithms. For instance, personal biases of the individuals
labelling data for a machine learning model may be mirrored in the model. To
construct extensive data sets and various models to suit each use case, it is
crucial to identify the biases in your data.
Complexity and Compliance
It might be challenging to create efficient machine-learning
models that satisfy the objectives of the financial industry since financial
data is frequently complicated. The financial industry is very closely
controlled and inspected. Any machine learning model you use must take current
rules into account as well as any future legal, regulatory, and privacy issues.
Considering all the rules and the possibility that your system may be audited,
it is essential to create an MLOps plan that records your data sets and model
outputs for regulators to examine.
Hiring and Retaining Quality Employees
Across businesses, including financial services, there is a
dearth of expertise with the know-how to create and use efficient machine
learning models. Due to labour scarcity, businesses are forced to pay higher rates
or hire outside help, like an AWS data analytics and machine learning partner.
10 Applications of Machine Learning in Financial Services
Although machine learning is still in its infancy, the
financial services sector has already seen its benefits. Here are 10 ways your
company may start using it.
Fraud Detection
Rules-based systems that identify questionable transactions
based on predetermined criteria are used in traditional techniques of fraud
detection. These systems, however, are rigid, demand regular update, and
produce false positives. Machine learning, in comparison, can analyse data more
accurately and fast. To anticipate potentially fraudulent activity, it
recognises trends and abnormalities.
For instance, Amazon Web Services (AWS) provides Amazon Fraud Detector, which analyses transaction
data and produces fraud risk assessments in real-time using machine learning
algorithms.
Since fraud is a continuing danger, your methods of
detection must advance as dishonest individuals employ more complex fraud
techniques. Algorithms for machine learning assist in reducing reputational,
financial, and operational risks as well as fraud.
Customer Service
In the banking sector, machine learning and artificial
intelligence have made it simpler for businesses to deliver customer care. For
instance, chatbots and virtual assistants are able to comprehend and reply to
people's questions and requests by integrating machine learning techniques with
natural language processing (NLP), another branch of AI.
Customer care agents can now manage more complicated
situations thanks to these technologies. They also provide a different data
source that may be leveraged to provide tailored suggestions based on a
client's past choices and preferences.
Risk Assessment
Machine learning and artificial intelligence approaches help
speed up risk assessment processes without sacrificing accuracy. For instance,
loan underwriting benefits from applying machine learning to examine data for
trends or discrepancies that may point to fraud, probable loan defaults, or
other risk.
You can increase your risk assessment skills by using
services like AWS SageMaker. Machine learning's early-warning indicators can be
the difference between recognising hazards and having to deal with them later.
Trading and Investment Management
In order to find investment opportunities and forecast asset
values, machine learning algorithms may analyse both privately owned data and
publicly accessible data, such as market movements, news, and social media
data. Additionally, portfolio management and investment changes in response to
market conditions may be done using these algorithms.
Regulatory Compliance
To assure regulatory compliance, artificial intelligence and
machine learning approaches are being deployed. Financial institutions are
subject to a lot of regulation, and failure to comply can result in legal,
criminal, and reputational consequences. Machine learning techniques may be
used by AI-powered systems to quickly provide reports for regulatory bodies and
highlight any compliance issues.
Credit Scoring
A small number of informational factors, including as
payment history, amount of outstanding debt, and duration of credit history,
have long been used to construct credit scores. These standards don't always
provide a comprehensive or accurate picture of a consumer's creditworthiness.
For a more accurate evaluation and a lower chance of bias, machine learning may
examine a larger variety of data points, including online behaviour. Fairer
loan choices are made as a consequence, and borrowers who are disregarded or
even treated unfairly by conventional credit scoring methods have more options.
Data Security
The management of cybersecurity is tremendously difficult
for businesses, especially when they handle sensitive financial data. Machine
learning algorithms can examine user behaviour patterns to spot possible
security risks like shady logins or strange activity. Machine learning may
assist you in preventing security breakdowns as part of a comprehensive
cybersecurity and data protection programme rather than just responding to
them.
One effective security application that employs machine
learning to automatically find, categorise, and safeguard sensitive data kept
in AWS is Amazon Macie. This service uses suitable security mechanisms to
secure your data, such as encryption and access restrictions, to lower the risk
of breaches.
Algorithmic Trading
Computer programmes are used in algorithmic trading to carry out trades in accordance with established rules and algorithms. Machine learning may be used to analyse market data, spot patterns, and help develop more profitable trading methods.
Moving these kinds of solutions to the cloud is a new trend
in the sector. The motivation for financial institutions to relocate is greater
than exchanges and data suppliers do. This enables them to be nearer to others,
take use of the cloud's many advantages, and have low latencies, which are
necessary for high-frequency trading. Utilising resources such as Amazon SageMaker and AWS Data Exchange, these solutions may be created and
implemented. Consider a migration readiness evaluation if your company is
considering making this move.
Marketing
You might not immediately associate marketing with machine
learning. However, by taking lessons from algorithms that examine client data,
behaviour, and preferences, your company may enhance its marketing initiatives.
Your company may create individualised marketing campaigns and product
suggestions with the use of sound machine learning techniques.
You may further customise campaigns by using other
artificial intelligence tools. NLP may be used, for instance, to analyse
consumer feedback via encounters, online reviews, and social media. You may
modify your messaging to better match the demands of your audience by using
this data to uncover client preferences or areas where they might be unhappy.
Customer Experience
The total customer experience may be enhanced through
artificial intelligence and machine learning. Virtual assistants and chatbots,
for instance, may offer speedy and effective customer assistance. Because they
sense a connection to the products being given, personalised suggestions
encourage clients to make purchases. Additionally, proactive solutions may be
provided by using machine learning algorithms to anticipate client wants.
What's Next for Financial Services Firms?
Machine learning is obviously becoming more and more
significant. Machine learning in banking will continue to see new applications. Future machine
learning algorithms are likely to improve company decision-making, automate processes,
simplify operations, and continue to personalise the consumer experience.
The lack of qualified personnel to fill key positions such
as data scientists, analysts, and other crucial positions will remain one of
the industry's largest issues. Find a dependable third party who can provide
you with the resources you want, or be prepared to compete for machine learning
talent.
Finding methods to integrate artificial intelligence and
machine learning into your current business processes and workflows is the key
to effectively using these technologies in the financial services industry.
Determine the precise inefficiencies and pain areas in your business, then
investigate how machine learning might assist you in resolving these issues.
