A Guide to Integrating ML into Fintech Business: Cases and Applications

Innovative AI and ML technologies disrupt conventional fintech and raise additional challenges and opportunities for businesses in this niche. Business owners are now naturally curious about how they utilize machine learning in finance to their company’s benefit, and whether ML can offer a good return on every dollar invested in it. 

McKinsey’s report shows that about 55% of businesses worldwide have already adopted AI and ML in at least one of their everyday operations. The prospects are that the percentage of innovation-first companies, as well as the number of operations enhanced with ML, will rise gradually during the next several years. 

In this post, you’ll discover more about how ML in fintech works and what the applications of ML in fintech are. You’ll also review the value-adding benefits of adopting ML and what machine learning use cases in finance are there. 

What is ML in Fintech and How Does It Work?

Let’s begin by recalling the basic terms foundational to the topic: 

ML is a subset of AI that enables computers to learn from data, identify patterns, and make decisions with minimal human intervention, improving their accuracy over time.

ML, as a part of the trending technology, has already had a transformative impact on nearly all domains. Here’s what it means in fintech:

**ML in fintech is the technology focused on enhancing customer service and operational processes in fintech and financial businesses. **

Within the financial services industry, ML is typically used to automate processes, enhance decision-making, and provide personalized services to consumers.

You can discover ML algorithms and techniques used in fintech in the below infographic:

What Applications of ML-based Fintech Solutions Are There

Now, let’s get to know what applications of ML in fintech are available in the form of technology and can be used to benefit your business. This will show you the rough scope of solutions you can consider creating and adopting in your operational processes.

• Customer service and support

What is it: Handling inquiries and account management tasks 24/7. Offering basic financial advice, thereby improving service availability and reducing response times.

What ML solutions to use: Chatbots and virtual assistants. 

• Predictive lead scoring

What is it: A method to evaluate potential customers’ likelihood to convert based on their behavior and historical data points. This enables targeted marketing efforts, optimizing resources towards high-potential leads.

What ML solutions to use: Predictive analytics models that assess customer interactions, engagement patterns, and conversion histories to score leads for prioritization.

• Automated risk management

What is it: The use of machine learning algorithms to assess, identify, and mitigate risks in real time. This includes analyzing market trends, customer behavior, and transaction data to prevent potential threats.

What ML solutions to use: Machine learning models trained on particular data points to recognize patterns leading to the emergence of risk. 

• Fraud detection and prevention

What is it: The application of machine learning algorithms to analyze transaction data and identify anomalies that may indicate fraudulent activities, thereby enabling quick preventive actions.

What ML solutions to use: Anomaly detection algorithms and predictive models that scrutinize transactions to flag irregularities and potential fraud.

• Personalized fintech product usage experience

What is it: Tailoring financial products and services to individual customers based on their preferences, behavior, and financial history to enhance satisfaction and engagement.

What ML solutions to use: Recommendation engines and personalization algorithms that analyze customer data points to offer customized financial advice, products, and services.

• Insurance claim processing

What is it: The automation of processing insurance claims, including data entry, document verification, and initial property assessments, to streamline procedures and improve accuracy.

What ML solutions to use: Natural Language Processing (NLP) for document analysis, Computer vision for visual damage assessments, and predictive models for initial claim assessments.

• Data management and document processing

What is it: The automation of extracting, organizing, and analyzing information from vast amounts of documents to reduce manual work and improve data accuracy.

What ML solutions to use: NLP for document understanding and extraction and machine learning models for data categorization and analysis.

Although the option to integrate all of the listed may seem lucrative, businesses, especially startups, usually stick to only a few of the highest-priority ML applications, building their digital product around them. So, prioritize one ML solution and stick to its development and integration first. 

Value-Adding Benefits of Using ML in Fintech

Source aiqrate.ai

Here are the value-adding advantages you can get from utilizing AI in fintech with an indication of AI solutions enabling each of them:

• Better strategic decision-making. Predictive analytics and big data analysis provide insights into market trends, customer behavior, and potential financial risks. This leads to smarter, data-driven strategic planning.
• Improved security. ML-driven fraud detection, anomaly detection, and transaction monitoring solutions can identify suspicious activities. This leads to protecting both fintech businesses and their customers from potential threats. 
• Full regulatory compliance. ML helps fintech companies achieve compliance with data security standards like HIPAA and GDPR through automated compliance monitoring and reporting tools. It helps to earn customer loyalty and trust and to avoid penalties for non-compliance of up to 4% of annual revenue.

You may be interested in learning additionally about standard compliance and other regulatory changes that the third Payment Service Directive (PSD3) brings to the financial industry.

• Cost reduction. Process automation solutions built with ML in fintech industry reduce the need for manual labor involvement to a certain extent. This results in significant cost savings and the simplified management structure required for the business. Staff costs amount to 27.8% of all operations costs in fintech, on average. You can imagine what expense reduction optimizations here can bring. 
• Better risk management. ML-based predictive models and risk assessment tools enable accessing, understanding, and managing financial risks. It leads to forming better risk mitigation strategies and better risk factor prediction.
• Enhanced customer experience. Chatbots and personalized financial advice systems enable to provide 24/7 customer service and tailored product offerings. This results in a more engaging and satisfying experience for users, meeting their needs more effectively. 

Source: Netomi

• Greater competitiveness in the market. In modern-day reality, the adoption of ML-based technologies is considered nearly obligatory. The technological edge translates into better capabilities to compete in the evolving market, resulting in better business performance.

“The future belongs to those who can rise above the technology and master it.” – Jamie Dimon, CEO of JPMorgan Chase.

The interesting point about the implementation of ML solutions in businesses is that they not only provide a direct advantage depending on the type of software, whether it’s fraud detection or predictive lead scoring. They also enable your business to join the ranks of innovation-first companies that typically benefit from higher customer satisfaction and brand loyalty. 

After all, nearly all businesses are expected to adopt smart technologies. The only question is, will your businesses be among the first adepts of ML in your niche?

You may additionally be interested in learning about how to execute IT modernization in fintech. 

Challenges of implementing ML in FinTech

To embrace the pros and cons of applications of ML in fintech, it’s crucial to understand the challenges that commonly arise in ML projects. Here are some of them: 

• Data quality and availability issues

The usage of machine learning technology requires large volumes of high-quality, diverse, and representative data to learn effectively and make accurate predictions. In Fintech, obtaining such data can be challenging due to inconsistencies, inaccuracies, and gaps in financial datasets. There is even a simple rule that showcases the importance of proper data in any application ML. It’s called “Garbage in, Garbage out,” or simply GIGO.”

A huge part of the challenge is establishing the system for retrieval, processing, and storing data, i.e., data pipelines and the infrastructure behind them, which presents a complex technical challenge. 

• Ethical and bias concerns

ML algorithms can inadvertently learn and perpetuate biases present in their training data, leading to unfair or discriminatory outcomes. This is particularly problematic in Fintech applications such as credit scoring, where biased algorithms could result in unfair denial of services to certain groups.

It’s worth remembering that ML-based systems lack human empathy and can gather and share data in an unethical manner. 

• Data privacy and security concerns

The implementation of ML in FinTech involves processing vast amounts of sensitive personal and financial data. Ensuring the privacy and security of this data is paramount, as breaches can lead to significant financial and reputational damage.

ML systems don’t distinguish between information that is available for public usage and information that must be available only to a limited number of users. 

• High implementation costs

The development and deployment of ML solutions can be costly, involving expenses for data acquisition, model development, computational resources, and talent acquisition. Additionally, ongoing maintenance and model updates further contribute to the costs.

Custom, innovative, and large-scale ML models present a technical challenge to create, resulting in high implementation costs. 

Now, you know the score. However, knowing the potential obstacles should not undermine your willingness to get started with your machine learning finance projects. After all, you can address the listed and other challenges by partnering with a trusted fintech firm that has expertise in ML and in the software solution you intend.

3 Real-Life Cases of ML in Fintech Industry

To understand how you can apply ML capabilities in practice, it’s crucial to be familiar with real-life machine learning use cases in banking and finance, which are already there. This will provide you with an insight into how the capabilities and applications listed above can be mixed to form a concrete business solution to an existing customer need. 

So, here are three cases showcasing different ML applications in various businesses:

#1 Application of NLP and Text to Speech ML: Call Translator Case by DashDevs

The customer’s request was to create an ML-based tool for real-time, automated translation of speech to multiple languages. It was intended to be used with conferencing software like Google Meet or Zoom.

The DashDevs team utilized a mix of Natural Language Processing, Text-to-Speech, and Speech-to-Text ML-based functionalities to enable the understanding of speech and its conversion to text and back to voice. Besides, the end solution was reinforced with Generative AI integration to conduct the translation of text from the proceeded speech to any of the vast array of languages supported. 

The implementation of the solution called Call Translator results in an average increase of 35% in time efficiency and a nearly 50% decrease in error rate when handling tasks.

#2 Application of NLP and ML-Based Anomaly Detection: Anti-Money Laundering (AML) Case by Lucinity

Lucinity’s objective was to enhance the efficiency and accuracy of detecting money laundering activities within financial transactions. They aimed to develop a solution that could sift through vast amounts of transactional data to identify potentially illicit activities indicative of money laundering.

In this application of ML in finance, Lucinity harnessed the power of NLP and ML-based anomaly detection technologies. The core of the solution involved NLP techniques to understand and categorize the context and intent behind transactions, complemented by ML algorithms designed to recognize patterns and anomalies that deviate from typical transactional behavior.

Lucinity’s AML solution, provided in the form of a fully-fledged AML platform, is now one of the company’s main digital products. The platform helps businesses lower the operational costs associated with AML compliance by automating the detection and reporting process.

#3 Application of ML-based Document Processing: Automated CV Parsing Case by DashDevs

The DashDevs aimed to simplify the processing of CVs done by the HR department by automating CV processing and reformatting as per new templates. 

The DashDevs team utilized a library for image processing to enable the retrieval of text from PDF-based CVs. Besides, we utilized an ML model for document processing to automate the filling of new CV templates with the retrieved information. 

The resulting solution greatly streamlined the processes of CV parsing and filling in new templates for the HR department. The implementation of our solution saves up to 50 working hours annually on average. 

While there are some pre-created ML-based solutions and platforms you can consider integrating into your operations or digital products, many businesses still need custom development of ML features. 

So, the main question here is, “What do you do to get started with your ML project in fintech?”

Partnering with a trusted provider of ML development services in fintech is the right call for most companies, regardless of their size. After all, external tech providers can:

1. Bring expertise your in-house team may lack
2. Free up time and resources for you to work on your core offering
3. Run the entire project with your minor involvement. 

Final Take

As of now, machine learning in banking and finance is trending. With most businesses utilizing smart technology in at least one of their operations and a lot of companies investing in fully-fledged custom machine learning solutions, it’s safe to claim that the solution built on this technology has promising potential as an investment project. 

Partnering with a trusted team specializing in ML development in fintech. Here at DashDevs, we have been providing fintech services for more than 12 years, assisting businesses worldwide. With over 500 projects completed, we are ready, willing, and able to contribute to your best ML project. 

Contact us