Machine learning in finance: Future trends and use cases
Artificial intelligence, with its numerous offspring (generative AI, computer vision, natural language processing, and more), is one of the main high-tech advancements that pushes the envelope across multiple industries. Machine learning tools based on neural networks set the pace in this field, being harnessed by enterprises far and wide.
According to Cognitive Market Research, the financial industry readily employs deep learning and machine learning models in its workflows, with 72% of financial services companies reporting utilizing ML applications as elements of their IT infrastructure. And the adoption of this technology by financial institutions grows exponentially. The latest research claims that the global ML market in the finance industry, which was worth $7.52 billion three years ago, is predicted to increase more than five times by 2030, displaying an astounding CAGR of 22.5%. If your organization still lags behind other finance companies in this aspect, it is time to make up for this gap.
The article showcases possible applications of machine learning techniques in the financial services industry and explores roadblocks companies can encounter while embracing machine ML-driven solutions.
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Obstacles in adopting machine learning in financial services
As a vetted IT vendor specializing in machine learning development, we at DICEUS realize what problems finance companies face in developing and onboarding ML products.
Obstacles to adopt ML
Talent dearth
Every innovation requires a qualified workforce for its implementation, and machine learning is no exception. And like it happens with any other novel technology, such specialists are hard to come by. With AI-based know-how, the lack is more evident since the development of financial ML applications is performed not only by machine learning talent but rather by a big team of experts proficient in data science, fintech, large language models, natural language processing, and other cutting-edge technologies. Financial institutions should put great effort into hiring professionals capable of crafting high-quality solutions that fit their business purposes.
Considerable investment
The shortage of specialists conditions premium salaries they demand for their services. Plus, the software and hardware necessary to implement machine learning are not a chump change issue either. That is why financial companies poised for big-time success should allocate sizeable budgets for ML-driven business transformation. For some small-size organizations, the cost may turn out to be forbidding.
Data availability and quality
Whether you opt for supervised learning, unsupervised learning, or reinforcement learning for ML model training, the accuracy of outcomes depends critically on the data utilized in the process. You should make sure you have access to all relevant information and store it under one virtual roof. After data collection is completed, it should be curated to guarantee its consistency, timeliness, understandability, integrity, and compatibility with machine learning models where it is going to be used.
Data bias
Another possible problem with data is the unintended bias it contains. When such records are leveraged for training machine learning algorithms, this bias will penetrate into the solution’s performance results, distorting them or perpetuating discriminatory practices concerning loan underwriting, portfolio management, investment planning, and the like.
The best way to mitigate such a bias is to pre-process the collected data meticulously, ensuring it consists of diverse and representative data sets, rely on fairness-aware ML models, employ a different model for each use case, and perform regular audits of algorithm operation.
Model validation
Typically, AI testers and QA engineers employ backtesting to validate the functioning of machine learning models, checking their performance against historical data to understand how it would have operated and ensure their viability. However, in the financial realm, historical data becomes obsolete very quickly, making backtesting an inadequate model validation technique and urging experts to look for other methods that can take into account real-world scenarios and make allowance for data drift.
Market volatility
The current financial sector is a highly dynamic field subject to sudden changes, black swan events, and sentiment shifts. Even the most sophisticated ML models can’t include such factors in their calculations and perform 100% accurate predictions because of them.
Integration with existing systems
Many financial institutions rely on legacy software and outdated equipment in their pipeline operations. Cutting-edge machine learning solutions don’t play well with such infrastructure, so their implementation requires redesigning the entire ecosystem. It is a time-, money-, and effort-consuming ordeal necessitating at least partial disruption of shop floor routines. However, this is the price you have to willingly pay to provide seamless communication between all components of your professional digital environment.
Regulatory compliance
Before ML tools can help with monitoring the compliance of the organization’s workflows, they must use tons of data to learn how to do this task. And this training data is also protected by GDPR, CCPA, and other statutory norms adopted in the industry. Financial institutions implementing machine learning software should take care the sensitive data employed by their systems is secure at rest and in transit and that no unauthorized person has access to it.
Being quite serious, these challenges are more than offset by the perks machine learning tools can usher into various financial operations.
Top 10 ML use cases in financial services
ML-powered solutions are intensively leveraged in the following shop floor operations in the sector.
ML cases in financial services
Fraud detection
Traditionally, fraud-detecting systems were rule-based solutions that utilized pre-determined criteria to identify suspicious activities. Such operating principles made them inflexible and often inaccurate as well as required constant updating.
By contrast, machine learning algorithms have the ability to update themselves constantly in real-time without human intervention. Trained on numerous examples of fraudulent behavior, they can pinpoint patterns, remembering what is normal and what is not, and red-flagging potentially fraudulent activities, such as insider trading or money laundering. And the more data points they process, the more accurate their performance becomes.
Enhancing customer support
Here, machine learning teams up with natural language processing, enabling artificial intelligence to revolutionize customer service. This disruptive combo is highly instrumental in creating chatbots and virtual assistants. The simpler variations of them are employed for customer acquisition and onboarding automation since they understand only a limited number of messages, whereas more sophisticated ones can handle complex requests and queries, recognize customer emotions during the interaction, and offer relevant recommendations.
Risk assessment
The financial domain is fraught with various risks, ignoring which can cost companies a lot in terms of monetary and reputational losses. With ML-driven tools in place, they can analyze large data sets and employ advanced statistical analysis, linear regression, logistic regression, and other methods to foresee risks and determine the probability of their occurrence. It allows companies to set up early warning systems and take proactive measures, minimizing potential damage.
Managing trading and investments
In this aspect, machine learning relies on its predictive power in several ways. ML-fueled solutions analyze large amounts of data from all available sources (official reports, news, social media, etc.) and derive actionable insights from them. These are used by companies to predict stock prices, reveal investment opportunities, and adopt knowledgeable trading decisions. When geared for individual decision-making, such ML tools known as robo-advisers help investors improve their personal portfolio management and devise tailored trading strategies, taking into account the client’s risk tolerance.
Ensuring compliance
The financial sector is one of the most heavily regulated fields, operations in which should be conducted in line with stringent laws concerning data privacy and money-laundering prevention. Machine learning algorithms can be configured to check whether the company’s practices comply with such norms and generate respective reports for regulatory bodies. Moreover, ML tools can help monitor changes and updates in the current legal framework to bring the organization’s pipeline in accordance with them.
Credit scoring and loan underwriting
Before opening a credit line or giving a loan, banks and other financial institutions must be sure the person or enterprise will return the money. ML solutions can establish a consumer’s creditworthiness by analyzing various records, including payment history, sources of income, investment portfolio, online behavior, and more. Such an approach allows lending analysts and data scientists to quickly obtain precise credit scores and rules out unfairness or bias during the loan decision-making process.
Securing data
Cybersecurity is probably the top concern for all digitally-driven businesses of the early third millennium. Given the amount of sensitive personal, financial, and business data fintech companies process and the possible damage their leakage can cause, protecting it is mission-critical for organizations in the realm.
Machine learning tools are a good crutch in this endeavor. First of all, they can identify which data requires reinforced protection measures and apply them by performing its encryption and restricting access to it. Second of all, ML algorithms can become part and parcel of your cyber security programs that are responsible for detecting potential threats (such as unusual activities or suspicious logins) and nipping penetration attempts in the bud.
Algorithmic trading
It is an automated practice of buying and selling assets. It has a considerable edge over human trading since ML tools analyze tons of data simultaneously and use probabilistic logic to perform thousands of transactions a day. Moreover, they exclude any emotional factors in the process and do everything lightning-fast during high-frequency trading, where machines perform multiple transactions very quickly, responding to fluctuations in financial markets.
Optimizing marketing efforts
Having machine learning solutions in your arsenal, you can step up your marketing routines immensely. ML-powered tools will help you create a 360-degree picture of your customers with their demographics, purchasing behavior, tastes, and pain points, enabling you to administer personalized marketing campaigns and tap new cross-selling and upselling opportunities.
Improving customer experience
When dealing with brands, consumers appreciate being special and unique. Machine learning software can create this feeling, using information about them to predict their individual needs and offer tailored recommendations of products and services that dovetail with their expectations. As a result, customer satisfaction grows, turning one-time visitors into loyal clients and even brand advocates.
You can enjoy all fortes of machine learning in these and other financial workflows by partnering with a reliable IT vendor competent in fintech development and having in-depth expertise in AI-based technologies. Having completed numerous projects in both domains, DICEUS meets these requirements. Our software developers possess broad theoretical knowledge and excellent hands-on skills to deliver the best-in-class custom ML-driven fintech solutions that will become a game-changer in all business operations for your financial institution.
Drop us a line to boost your shop floor routines with the power of machine learning.
Final thoughts
Machine learning is a disruptive technology that uses the power of artificial intelligence to learn from past experiences. In the finance industry contexts, ML tools can be employed to reinvent the majority of workflows in the domain, including fraud detection, credit scoring, loan underwriting, risk assessment, investment and portfolio management, algorithmic trading, customer experience personalization, marketing, regulatory compliance, ensuring data security, providing efficient customer support, you name it.
To maximize the value of ML-fueled financial software, you should address key challenges of its implementation (lack of qualified talent, significant expenditures, data availability, quality, and bias, model validation, market unpredictability, integration with legacy software, etc.) and hire a competent and reliable IT vendor to implement the project.
Frequently asked questions
Why is machine learning important in the financial sector?
By implementing machine learning solutions, finance organizations streamline and facilitate the lion’s share of business processes across major workflows in the industry, such as fraud detection and prevention, risk assessment, trade and investment management, regulatory compliance, loan underwriting and credit scoring, algorithmic trading, marketing effort personalization, providing rock-solid data security, enhancing customer support, and more.
What are the main challenges of implementing machine learning in financial services?
Financial organizations that want to make machine learning tools a part of their professional infrastructure should handle such obstacles as lack of qualified workforce, significant initial investments, data availability, quality, and bias, market unpredictability, model validation, new software integration with existing legacy systems, and regulatory compliance.
How does machine learning improve fraud detection in finance?
In the extremely dynamic world, where fraudsters invent ever new ways of deceiving financial organizations and laundering money, traditional rule-based solutions governed by pre-determined criteria prove ineffective. ML-driven algorithms are trained on numerous instances of fraudulent activities, so they can recognize them accurately and quickly across large data sets and become more sophisticated in identifying such cases while analyzing more data.
What steps can financial institutions take to overcome data bias in machine learning?
Biased and unfair data used for ML model training can have a negative impact on the accuracy of results and predictions. To mitigate it, financial institutions should curate the collected data thoroughly to ensure it includes representative and diverse data sets, utilize a different ML model for each use case, prioritize fairness-aware models in their practices, and conduct frequent algorithm performance audits.
