How is machine learning used in finance?
From screening and approving loans to managing assets and preventing fraud, machine learning plays a crucial role on many levels in financial institutions. In this blog post, we’ll explore some ways that machine learning improves business processes in the financial sector.
Customer segmentation
Machine learning algorithms are far more effective for personalizing your customer experience than entire teams of employees. Simple demographics can’t fully explain actual consumer behavior, so financial organizations should use machine learning to segment consumers by their level of sophistication and financial acumen, and then customize products and services accordingly. All relevant customer interaction data is used to train these algorithms, which then automatically builds statistical models that help correlate customers’ preferences with their demographic, behavioral, and other characteristics. “Next Best Offer” recommendation The “Next Best Offer” strategy can provide personalized financial product and service recommendations for each customer by analyzing past behavior. This technique uses collaborative filtering (CF), a specialized component of machine learning. User-based CF uses the opinions and behavior of similar customers to predict a specific customer’s inclination towards purchasing a specific product, while product-based CF identifies products that customers have exhibited a similar preference for. If executed properly, this is a win-win approach: customers get their desired products and services, and financial institutions develop valuable relationships with their customers.
Customer churn prediction
To reduce customer attrition, financial organizations need a way to find patterns in customer activity that predict dissatisfaction and churn. Once you have identified the factors that might convince a specific customer to close their account or decide against renewing their service, you can take appropriate action to prevent lost business. Machine learning techniques can be used to analyze many more relevant dimensions and data points than a team of humans can, and they often reveal patterns that are invisible to unaided analysis by trained data scientists.
ATM cash optimization
To accurately predict cash demand for each ATM in a network, it is crucial to develop an intelligent cash management system based on machine learning that can help financial institutions lower operational costs and improve the return on their cash assets. ATM cash management has usually been performed manually, but financial institutions have begun to apply machine learning to historical cash withdrawal data to forecast cash demand on a per-ATM basis with unprecedented accuracy. Used in machine learning applications as approximators, artificial neural networks (ANNs) help avoid instances of ATMs running out of cash without allocating large amounts of excess cash to sit unused in ATMs. Machine learning also helps improve operations by determining optimal replenishment schedules and could even recommend the most profitable locations for future machines.
Underwriting
Underwriting is a core process for insurance companies, and finding and training competent underwriters are effortful and time-consuming tasks. Furthermore, even expert underwriters are ultimately human beings, so unconscious bias and costly mistakes are an inevitability. Underwriters have begun using machine learning techniques to better gauge risk and provide more accurate premium pricing. A machine learning agent can be trained to use historical data for underwriting evaluations and statistics to evaluate an underwriting decision and provide a certainty rating (from 0 to 100 percent certain) for its evaluation. This helps human underwriters decide whether to accept or adjust the machine learning agent’s evaluation. Using the agent’s evaluation reduces underwriters’ workload, enabling them to focus on more complex evaluations that were assigned low certainty ratings by the agent.
Customer service
Customers nowadays expect prompt access to accurate information and rapid issue resolution. To cater to this, banks are rolling out chatbots and investment service applications for smartphones that provide customers 24/7 access to financial services and support. Modern chatbots use sentiment analysis to adjust their response depending on a customer’s apparent goal, emotional tone, and other relevant factors. Machine learning is used to detect common conversational themes and improve customer service over the long run. Financial institutions also use machine learning to anticipate customer behavior and generate predictive insights that improve customer service and reduce attrition. By mining patterns in transaction data, companies can accurately forecast demand and personalize products and services. Millennials, the demographic cohort that will soon be the primary target of financial institutions, find these capabilities especially appealing. Leveraging them gives banks and financial institutions the opportunity to secure more clients than competitors who continue to rely on traditional online banking portals.
Fraud detection
Detecting fraud involved having a sizeable team of skilled professionals dedicated to the task. This was costly and time-consuming. Automation efforts over the last century focused primarily on using computers to create and run through lists of hundreds of compliance rules. If a particular document or account failed to follow any of these, the fraud detection software raised a red flag. While this checklist-style solution works well for known forms of fraud that are easy to define as a set of rules, it doesn’t help financial institutions find instances of fraud that lack a formal definition. Machine learning significantly reduces security susceptibilities in finance by leveraging copious volumes of data and utilizing supervised and unsupervised learning to detect fraudulent transactions and discover new patterns in transaction history that might suggest fraud.
Process automation
Traditional automation techniques have been improving operational efficiency in financial organizations for decades. However, these older methods lack the agility required to adapt to change. As customer preferences, regulations, and other market forces continue evolve, business processes and workflows need to be adjusted, too. Using machine learning in process automation allows an organization’s information systems to detect changes in the types of requests they process and make changes to the way they handle those tasks. For example, if a traditional automated system receives an invoice that doesn’t conform to a particular format, it forwards this exception to a human worker for further processing. After that human worker performs the correct series of steps, the invoice re-enters the automated workflow. No matter how many times similar exceptions occur, a traditional system will continue to forward them to a human, which essentially turns that automatic process into a manual process. However, if you infuse this automated system with machine learning, then the system observes the steps that the human worker performs on the exceptional invoice and uses that information to adjust its behavior. It is then able to detect future instances of this new type of invoice and process them correctly. Machine learning finds patterns among the exceptions and forms new rules for the system to follow.
Content creation
Machine learning has begun to assist humans in creating financial summaries, company profiles, stock reports, and other business documents. Instead of spending hours or days producing these documents, machine learning algorithms can draft them in minutes. They use hundreds of samples of similar content (written by humans) to compose text that sounds natural, uses the right tone, and follows other context-appropriate writing conventions. By using content creation solutions powered by machine learning, business executives can reduce the hours they spend each day preparing business letters and emails to the mere minutes it takes to review computer-generated text. Any corrections that they make will also feed back into the machine learning algorithm to continually improve its performance.
Conclusion
The use cases for machine learning in finance are both numerous and highly valuable. Not only does it help businesses to customize their customer experience but enables them to provide personalized products and services based on consumer behavior. Machine learning techniques predict and help reduce customer dissatisfaction and churn, while accurate prediction of cash demand in ATMs helps control costs and improves the return on cash assets. Chatbots provide round-the-clock customer service with superhuman speed and consistency. Machine learning underwriting agents help human underwriters make the best use of their time and effort. Fraud detection algorithms help find suspicious activity that even the best human experts might miss. Machine learning process automation keeps improving as new requirements emerge. Even business correspondence isn’t an exclusively human activity any more. Machine learning ensures that these digital financial solutions continue to perform correctly, even as the needs of financial institutions evolve substantially over time.
Reference: https://www.visionetsystems.com/blog/how-machine-learning-used-finance