10 Use Cases for Machine Learning in Financial Services

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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. 

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