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Simplifying Finance with Unsupervised Machine Learning in Capital Markets


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When you hear about machine learning, your thoughts may drift to captivating examples such as autonomous vehicles navigating busy streets or virtual assistants responding to our voice commands. However, the reality of machine learning today is far more practical and holds incredible potential to enrich various aspects of our lives, including managing personal finances and optimizing everyday tasks.

Machine learning has significantly transformed the finance and banking sectors, revolutionizing areas ranging from customer service to investment strategies. So, how can your fintech business effectively incorporate this innovative technology into its products and services to drive tangible results?

In this article, I will explore 5 of the most popular real-world machine learning use cases in finance and banking, highlighting the opportunities it presents for the fintech industry while addressing the challenges that must be overcome.

Key Insights on Machine Learning in the Finance Industry

Before delving into real-life examples of machine learning usage in the banking and finance sectors, let’s take a look at some compelling statistics. Analytical reports highlight a significant finding: machine learning in finance is maturing and holds the potential to deliver sophisticated solutions for financial firms that generate positive returns on investment across various business segments.

Here are some noteworthy facts we have highlighted:

  1. The adoption of AI and machine learning solutions in the finance industry is rapidly gaining momentum. According to the Cambridge Centre for Alternative Finance and the World Economic Forum, many financial services companies have integrated this technology in areas such as risk management (56%), revenue generation through new products and processes (52%).
  2. A forecast by research company Autonomous Next reveals a promising outlook for banks worldwide. By leveraging artificial intelligence technologies, these financial institutions have the potential to achieve a substantial 22% reduction in costs by 2030, resulting in estimated savings of up to $1 trillion.
  3. Insider Intelligence predicts that by 2024, the implementation of artificial intelligence and machine learning will be crucial for financial institutions to maintain a competitive edge and thrive in the market. The adoption of web and mobile banking among US consumers is projected to rise, reaching 72.8% and 58.1%, respectively.

It is also essential to note that machine learning and artificial intelligence have emerged as the most impactful trends in the fintech industry:

Emerging Trends in Banking and Finance: The Future Landscape

The banking sector is experiencing a significant surge in the integration of machine learning technology. According to Insider Intelligence, a remarkable 75% of respondents from banks with assets exceeding $100 billion are actively deploying AI and machine learning technologies. Hence, artificial intelligence and machine learning applications may present substantial cost-saving prospects within the realm of digital banking.

The Metaverse Revolution

This interconnected virtual realm of interactions and transactions is definitely poised to have a profound impact on the fintech industry. As the frontier of cryptocurrency, digital tokens, and NFTs, it is reshaping the landscape of online finance. The metaverse opens up a world of exciting possibilities, for example:

  • Enhanced customer engagement. If you interact with clients within the metaverse, they closely resemble real-life experiences, but with added smoothness and security. Your customers can actively participate in investor events, engage in bank-sponsored programs, or even plan their retirement investments with the guidance of an avatar advisor.
  • New products and markets. Banks can now insure and lend against virtual real estate, NFTs, and cryptocurrencies. People continue to invest in digital assets and utilize virtual currencies in the real world. Banks can sponsor virtual events, establish virtual branch storefronts, and even enable customers to withdraw funds from virtual wallets at ATMs. The potential is limitless and awe-inspiring!

Conversational AI for Enhanced Client Engagement

Today’s customers expect seamless communication and prompt responses from businesses. AI-powered interactions with financial institutions can fulfill these expectations. Leveraging data analytics, machine learning-powered chatbots can create natural and interactive experiences, delivering real-time problem-solving and highly personalized interactions.

Low Code and No-Code AI Solutions

With the aid of low-code or no-code AI tools, your organization can develop customized solutions while reducing software development costs and time-to-market. In fact, according to Gartner’s research, 65% of organizations are planning to embrace low-code or no-code solutions, allowing them to swiftly adapt to industry changes.

Even if you don’t have extensive coding experience, you can create, modify, and update applications that provide a seamless customer experience with these user-friendly platforms.

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5 Use Cases of Machine Learning in Banking and Finance

As the banking industry continues to evolve, the utilization of machine learning (ML) has become a key driver for enhancing customer experiences and optimizing back-office operations. ML tools can enable banks to unlock valuable insights from their vast data streams, influencing various aspects ranging from operations to business development and marketing.

Typically, businesses in the fintech realm embrace ML use cases to achieve quicker support, fortified security measures, and streamlined processes. In this section, I’ll guide you through the top ML use cases that have gained popularity within the banking sector, highlighting their significant impact for your business.

Use Cases of Machine Learning in Banking and Finance

#1 Fraud Detection

As the ingenuity of hackers continues to evolve, banks face mounting pressure to proactively combat financial crimes, particularly fraud and money laundering. Machine learning algorithms possess the remarkable ability to process vast volumes of data within seconds. Their capacity to learn from past experiences and refine models minimizes the need for extensive human intervention. By leveraging ML algorithms, systems can rapidly detect suspicious activities and promptly notify the security operations center or automatically decline fraudulent credit card transactions.

Beyond traditional rule-based fraud detection methods, ML enables real-time scanning of massive data sets while reducing reliance on human involvement. This not only bolsters security but also significantly enhances the user experience by streamlining identity verification measures. Let’s explore how ML differs from rule-based fraud detection in terms of efficacy:

To illustrate the transformative potential of ML in combating financial crimes, I want to provide you with a few notable startups utilizing accessible machine learning tools to empower businesses in outsmarting hackers and financial criminals:

  •, a Prague-based startup, leverages ML to safeguard AI systems against targeted manipulation, adversarial machine learning attacks, and sophisticated fraud attempts.
  • TrustGuard deploys cutting-edge machine learning algorithms to offer payment insights that empower businesses to make informed decisions when accepting or rejecting transactions.
  • ShieldTech, a US startup, harnesses ML to develop advanced risk management tools that effectively prevent fraud and money laundering during transactions.

To sum up:

  1. Mitigating the risk of data breaches and cyberattacks through robust fraud monitoring.
  2. Enabling real-time security monitoring with minimal human intervention, ensuring seamless operations.
  3. Uncovering and responding to fraudulent transactions that may evade detection through manual rule-based systems.

#2 Compliance Regulations

Financial institutions worldwide face the critical task of adhering to regulations in order to expand, thrive, and stay competitive. However, compliance often feels like an enduring challenge as regulations change rapidly, sometimes even before previous ones are fully complied with.

The top three challenges many financial companies encounter due to these ever-changing compliance standards are:

  1. Operational disruptions
  2. Regulatory disputes and penalties
  3. Hindered or slower innovation

Here’s an interesting fact: In 2020, global financial institutions paid a record-breaking $10.6 billion in penalties for violations related to anti-money laundering regulations, know-your-client tests, and sanctions, attributed to Fenergo.

To navigate this landscape, machine learning (ML) has emerged as a savior for many financial institutions striving to achieve regulatory compliance. ML algorithms can analyze vast amounts of regulatory documentation and identify correlations between different guidelines. ML-powered systems can automatically detect and promptly respond to changes in laws as they arise.

Let’s explore a few fintech startups in this field:

  • empowers lenders to make informed decisions and better loans, leading to increased revenue, reduced risk, and streamlined compliance processes. With a remarkable funding record of $232 million across seven rounds.
  • Onfido is a renowned identity verification company that utilizes machine learning algorithms to verify user identities remotely. Their technology helps financial institutions comply with strict know-your-customer (KYC) and anti-money laundering (AML) regulations while providing a seamless user experience.
  • Sym addresses the gap between policy intent and execution, ensuring seamless alignment between workflows and compliance objectives.

To sum up:

  1. Automated monitoring of regulatory changes.
  2. Time-saving automation of manual processes associated with regulatory work.

#3 Risk Assessment

Machine learning in credit scoring revolutionizes the loan approval process for banks. By analyzing diverse data points, such as rent payments, social profiles, and bank history, algorithms can quickly generate a comprehensive credit profile. With such technology, your banking institution can assess risk efficiently and make informed lending decisions in a matter of minutes.

According to the McKinsey Global Institute, the potential financial impact of machine learning solutions for risk assessment in the banking industry exceeds $250 billion. So looking at these innovative credit scoring use cases, you can see the power of machine learning to drive financial inclusion and improve access to credit for individuals.

  • Kreditech, a startup that leverages predictive analytics and natural language processing to assess the creditworthiness of individuals with limited banking history. By analyzing alternative data sources, Kreditech provides valuable insights for determining loan eligibility.
  • ZestFinance, a US-based fintech company that specializes in using machine learning algorithms to assess credit risk. Their advanced underwriting platform analyzes a wide range of data points, including non-traditional data sources, to generate more accurate credit scores and expand credit access.

To sum up:

  1. Leveraging diverse data such as work experience, income, transactions, and credit history to generate personalized and reliable credit scores.
  2. Eliminating emotional bias allows for equitable evaluations, removing gender, racial, and other biases, resulting in faster loan approvals and enhanced customer convenience.

#4 Customer Retention

Imagine a scenario where a large bank receives a constant stream of support requests from customers. These inquiries often revolve around common issues such as transaction status, credit limit adjustments, and password changes. And there are only a few exceptional cases that require real-time assistance from support officers.

To address this challenge in your business, you can leverage machine learning to automate and expedite their support processes. By harnessing the immense data resources available, ML uncovers precisely what matters to each individual customer, enabling financial institutions to provide personalized assistance and tailored offers. Moreover, machine learning-powered solutions continuously learn from past interactions, enhancing their capabilities as they process more intricate customer data.

The very familiar example of using ML in banking to each of us are chatbots. These intelligent virtual assistants offer real-time, client-centric, and remarkably human-like support, resulting in an enhanced user experience while simultaneously conserving human and organizational resources.

Chatbots continuously learn from each customer interaction, making conversations increasingly personalized and helpful over time. Consequently, businesses in the finance sector, regardless of their size, can derive significant benefits from integrating chatbots into their operations, thereby reducing the dependency on traditional customer support departments.

  • MoneyMaster AI has developed an advanced chatbot that employs machine learning algorithms to provide personalized financial guidance and support to customers. Their AI-powered assistant is designed to address various customer inquiries and offer tailored recommendations based on individual financial goals.
  • DigitalBanking Inc., a banking institution, has implemented an innovative machine-learning-driven chatbot named FinAssist. FinAssist engages customers in natural language conversations, offering real-time assistance with banking transactions, account management, and personalized financial advice.

To sum up:

  1. Enabling rapid resolution of customer inquiries, minimizing waiting times, and enhancing overall satisfaction.
  2. Delivering tailored support, addressing customers’ specific needs and preferences.
  3. Leveraging vast amounts of customer data and providing data-driven insights and recommendations that ensure a more informed and effective customer experience.

#5 Stock Market Forecasting

By combining historical datasets with real-time information, machine learning becomes a powerful tool for predicting stock market trends. It can help us identify future risks and forecast stock prices, making it invaluable for financial modeling. The next step is to build a system that enables algorithmic trading. Let’s see how it works.

How it works

Machine learning has produced various models for forecasting stock prices, including those tailored to cryptocurrencies like SARIMA and FB Prophet. Although predicting the future of assets like Bitcoin may be challenging, ML provides us with a high degree of confidence in understanding the potential trajectory of stock market dynamics.

In the realm of stock market forecasting, some fintech companies have harnessed the power of machine learning:

  • Walnut Algorithms utilizes AI and ML advanced algorithms to analyze market trends and historical data to make informed predictions and optimize investment strategies.
  • DeepTrading, a UK-based startup, employs state-of-the-art machine learning techniques to analyze market trends, leverage sentiment analysis, and generate accurate predictions. Their platform offers comprehensive insights and actionable recommendations for traders and investors.

To sum up:

  1. Efficient processing of large stock data volumes, including real-time updates, for timely decision-making.
  2. Uncovering hidden correlations for accurate predictions of price fluctuations and market trends.
  3. Reliable forecasts surpass human predictions, leveraging advanced machine learning algorithms.

Summing Up

AI and machine learning have sparked a revolution in the banking industry, delivering enhanced safety, innovation, and efficiency to financial services. By harnessing the power of AI and ML, your fintech business can drive sales and create meaningful customer interactions. It all begins with targeting high-value use cases and gradually expanding their application throughout the organization. Embracing AI solutions is no longer just an add-on to existing processes; it has become an essential strategic imperative for achieving success in the dynamic global finance landscape.

Looking for a ML partner in fintech?
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If you’re seeking a reliable partner in the fintech industry, look no further. Our expertise lies in delivering top-notch custom software development services specifically tailored for the finance sector. More than 13 years ago, DashDevs evolved into a trusted provider of finance software development solutions. Discover our successful fintech projects and learn more about our capabilities in this dynamic field.

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