How to Apply Machine Learning in Banking: Top Use Cases

Banks, burdened by legacy systems and manual processes, are struggling to keep pace with the demands of a digital age. Here comes machine learning, a powerful technology that is poised to transform the way banks operate.

Imagine a world where loan applications are processed instantly, fraud is detected before it happens, and customer service is personalized to an unprecedented degree. This is the future of machine learning in banking.

According to a recent report, 75% of banks with over $100 billion in assets are currently deploying ML technologies. This trend is driven by the significant cost savings and competitive advantages that ML offers.

If you’re a bank looking to leverage the power of ML, partnering with a reputable machine learning consulting company is a great way to get started. A consulting company can help you identify your specific needs, develop a tailored ML strategy, and implement the necessary solutions. As for now, keep reading this article, we’ve collected all the necessary information about ML, its use cases and ways of implementation.

What is machine learning in banking?

Essentially, machine learning is the process of training computers to learn from data and make predictions or decisions without being explicitly programmed. In banking, this means leveraging vast datasets to uncover hidden patterns, identify trends, and automate processes.

Instead of relying solely on human analysts, banks can now leverage the power of machine learning algorithms to sift through mountains of information, from transaction histories to customer interactions. This enables them to spot anomalies, predict risks, and personalize services in ways that were previously unimaginable.

According to a recent report by the Cambridge Centre for Alternative Finance and the World Economic Forum, 56% of financial services companies are using machine learning for risk management. Furthermore, 52% are leveraging this technology to generate new revenue streams through innovative products and processes. These statistics highlight the growing importance of machine learning in the banking industry.

Top 10 use cases of machine learning in banking

Use case 1: Fraud prevention and anomaly detection

Traditional rule-based systems are often inadequate in detecting these evolving threats. Machine learning, with its ability to analyze vast amounts of data and learn from patterns, offers a more effective solution. This is how machine learning for fraud detection works:

• Data collection and analysis: Banks collect a massive amount of data from customer transactions, including historical data, behavioral patterns, and external information. Machine learning algorithms analyze this data to identify anomalies and potential fraudulent activities.
• Model training: Algorithms are trained on labeled datasets, where fraudulent and legitimate transactions are clearly identified. This training process enables the model to learn the characteristics of both types of transactions.
• Real-time detection: Once trained, the model can be deployed in real-time to analyze incoming transactions. It can identify suspicious patterns, such as unusual spending habits, inconsistent locations, or high-risk transactions, and flag them for further investigation.
• Continuous learning: Machine learning models can adapt to evolving fraud tactics. As new fraudulent patterns emerge, the model can be retrained with additional data, ensuring its effectiveness in detecting the latest threats.

Also, by monitoring employee behavior and access patterns, machine learning can identify signs of potential insider intrusions, such as unauthorized data access or suspicious activity.

Use case 2: Credit underwriting

Credit underwriting, the process of assessing a borrower’s creditworthiness, is a critical function in banking. Traditionally, this process has relied on manual methods and rule-based models, which can be time-consuming and prone to errors.

Imagine a bank that uses machine learning to analyze a borrower’s social media activity, online shopping habits, and bill payment history in addition to traditional credit bureau data. By combining these diverse data points, the bank can create a more nuanced picture of the borrower’s financial behavior and risk profile.

Traditional credit scoring models can be biased against certain groups of borrowers, such as people with limited credit histories or those from low-income communities. Machine learning models can be designed to be more equitable, reducing bias and expanding access to credit.

Also, machine learning models can process loan applications much faster than human underwriters, reducing the time it takes to approve or deny a loan. This can improve customer satisfaction and reduce operational costs.

Use case 3: Document processing

At the heart of machine learning-powered document processing lies Optical Character Recognition (OCR). OCR technology enables machines to extract text from scanned documents, transforming them into digital formats that can be easily processed and analyzed. Once the text is extracted, machine learning algorithms can be applied to classify and categorize documents based on their content. This classification process can be used for various purposes, such as identifying invoices, contracts, or customer identification documents.

One of the most prominent applications of machine learning in document processing is in the onboarding process. By automating the verification of documents, businesses can significantly reduce the time it takes for new customers to open accounts. Machine learning in banking can quickly analyze documents like identification cards, proof of income, and address verification, ensuring compliance with regulatory requirements.

Beyond onboarding, machine learning can also be used to enhance other document-intensive processes within banks. For example, it can be employed to extract key information from loan applications, automate the processing of insurance claims, and even detect fraudulent documents. By automating these tasks, banks can improve efficiency, reduce errors, and gain valuable insights into their operations.

Use case 4: Task automation

One prominent example of machine learning in process automation is robotic process automation (RPA). RPA software can mimic human actions to automate tasks such as data entry, and customer onboarding. This not only saves time but also reduces the risk of errors associated with manual labor.

Another area where machine learning excels is in customer support. By leveraging natural language processing (NLP), machine learning models can understand and respond to customer inquiries in a human-like manner. This can automate routine tasks like answering frequently asked questions, reducing the workload on human agents and improving customer satisfaction. Furthermore, machine learning can analyze customer interactions to identify trends and anticipate future needs, allowing banks to proactively address customer concerns.

Use case 5: Anti-money laundering

One of the key strengths of ML in anti-money laundering is its ability to adapt to evolving techniques. As criminals devise new methods to conceal their illicit activities, ML models can continuously learn and update their detection capabilities, ensuring they remain effective. This adaptability is crucial in a landscape where threats are constantly evolving.

Moreover, ML in banking can create dynamic customer risk profiles based on a variety of factors, including transaction history, customer behavior, and external data sources. By identifying high-risk customers, banks can allocate resources more efficiently and implement enhanced due diligence measures, reducing the likelihood of falling victim to money laundering schemes.

A recent example of ML’s application in AML is Google Cloud’s Anti-Money Laundering AI. This innovative solution leverages ML to generate a consolidated customer risk score, providing a more comprehensive assessment of potential risks compared to traditional rules-based approaches. By considering factors such as transactional patterns, network behavior, and Know Your Customer (KYC) data, the risk score can help banks identify high-risk retail and commercial customers more effectively.

Use case 6: Meeting regulatory compliance

ML algorithms excel at processing vast volumes of regulatory documentation, identifying patterns and correlations within the guidelines. This capability empowers financial institutions to proactively monitor regulatory changes, ensuring timely adaptation to evolving standards. By automating the analysis of regulatory updates, ML reduces the risk of non-compliance and minimizes the associated financial penalties.

One of the most significant advantages of ML in compliance is its ability to enhance risk management. ML models can analyze historical data to identify potential compliance risks and predict future trends. This proactive approach enables financial institutions to implement preventive measures, mitigating the likelihood of regulatory breaches and associated financial losses.

Use case 7: Chatbots for customer retention

One of the most prominent applications of ML in banking is the development of chatbots. These AI-powered virtual assistants can handle a wide range of customer inquiries, providing instant responses and reducing the need for human intervention. ML-powered chatbots can learn from each interaction, improving their ability to understand and respond to customer requests over time.

Financial institutions like Wells Fargo and Bank of America have been at the forefront of adopting ML-driven chatbots. These banks have successfully leveraged chatbots to provide personalized assistance, streamline customer support processes, and improve overall customer experiences. For instance, Bank of America’s virtual assistant, Erica, has gained immense popularity among customers, demonstrating the potential of ML in enhancing customer engagement.

Use case 8: Investment portfolio optimization

Traditional portfolio theory often relies on static assumptions about asset returns and correlations. In contrast, ML algorithms can dynamically analyze historical and real-time market data to identify patterns, trends, and potential anomalies. This enables them to construct portfolios that are better aligned with investors’ risk tolerance and return objectives.

ML also plays a crucial role in algorithmic trading. By analyzing historical market data and identifying recurring patterns, ML algorithms can develop automated trading strategies that execute trades at optimal times. These strategies can potentially outperform human traders by reacting more quickly to market movements and reducing the impact of emotional biases.

Furthermore, ML can be employed to enhance investment research. By analyzing news articles, social media sentiment, and other textual data, ML algorithms can extract valuable insights into market sentiment and company performance.

Use case 9: Stock market forecasting

​​A variety of ML techniques can be applied to stock market forecasting, each with its own strengths and weaknesses. Some of the most commonly used approaches include:

• Time series analysis: This involves analyzing historical stock price data to identify patterns, trends, and seasonality. ML models like ARIMA (AutoRegressive Integrated Moving Average) and Prophet can effectively capture these time-dependent relationships.
• Neural networks: Deep learning architectures, such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, are particularly well-suited for processing sequential data like stock prices. These models can learn complex patterns and dependencies within the data.
• Support vector machines (SVMs): SVMs are powerful classification algorithms that can be used to predict whether a stock price will go up or down based on a set of features.
• Random forests: This ensemble learning technique combines multiple decision trees to improve prediction accuracy and reduce overfitting.

Use case 10: Cybersecurity and training

One of the key applications of ML in banking cybersecurity is intrusion detection systems (IDS). Traditional IDSs rely on predefined rules to identify malicious activity, but ML-powered IDSs can adapt to new threats in real-time. By analyzing network traffic for unusual patterns, these systems can detect and alert security teams to potential breaches before they cause significant damage.

Furthermore, ML can be used to enhance security awareness training. By analyzing employee behavior and identifying areas of vulnerability, ML-powered tools can provide tailored training programs to help employees recognize and avoid phishing attempts, social engineering attacks, and other common threats.

How to implement ML in banking: key steps

1. Define clear objectives

The first step is to clearly define your goals. What problem are you trying to solve? Are you aiming to improve customer satisfaction, enhance risk management, or optimize operations? These objectives will guide your ML strategy and help you select the most appropriate algorithms.

2. Acquire and prepare high-quality data

High-quality data is the cornerstone of any ML project. Gather relevant data from various sources, including customer interactions, transactions, and market trends. Ensure data consistency, accuracy, and completeness. Cleaning and preprocessing the data, which often involves handling missing values, outliers, and inconsistencies, is crucial to prevent model bias.

3. Choose the right ML algorithms

The choice of ML algorithm depends on your specific objectives and the nature of your data. For example, supervised learning algorithms like regression and classification are suitable for tasks such as predicting customer churn or detecting fraudulent transactions. Unsupervised learning algorithms, such as clustering, can be used to identify customer segments or uncover hidden patterns in data.

4. Train and evaluate the model

Once you’ve selected an algorithm, train it on a representative subset of your data. This involves feeding the algorithm with input data and corresponding target variables. Continuously evaluate the model’s performance using appropriate metrics, such as accuracy, precision, recall. Experiment with different hyperparameters to fine-tune the model’s behavior.

5. Deploy and monitor

Once satisfied with the model’s performance, with the help of your ML services vendor deploy it into your production environment. Integrate the model into your existing systems and applications to make real-time predictions or decisions. Regularly monitor the model’s performance in the production environment to ensure it remains effective. If performance degrades over time, consider retraining the model with updated data or exploring alternative algorithms.

6. Address ethical considerations

ML models can have significant implications, especially in the financial sector. It’s essential to address ethical concerns such as fairness, bias, and transparency. Ensure that your models do not perpetuate existing biases or discriminate against certain groups. Regularly assess the model’s fairness and take steps to mitigate any biases.

7. Continuously improve

ML is an iterative process. As new data becomes available and business needs evolve, continuously improve your models. Retrain models with updated data, explore new algorithms, and stay updated on the latest ML advancements. By embracing a culture of continuous improvement, you can maximize the benefits of ML in your banking operations.

Machine learning development with PixelPlex

At PixelPlex, we specialize in providing comprehensive ML consulting and development services tailored to the unique needs of the banking industry. Our team of experienced data scientists and engineers is committed to helping you harness the power of ML to drive innovation and achieve sustainable growth.

Our ML services include:

• Machine learning consulting: Our experts will work closely with you to understand your specific business objectives and develop a tailored ML strategy.
• Machine learning development: We specialize in building custom ML models and applications that address your unique needs.
• Machine learning software integration: Seamlessly integrate ML solutions into your existing banking infrastructure.
• Machine learning modeling: Our data scientists will create and refine ML models to optimize performance and accuracy.

By partnering with PixelPlex, you can unlock the full potential of ML and gain a competitive edge in the banking industry. Contact us today to learn more about how our services can help you drive innovation and achieve your business goals.