An Industry at a Crossroads: AI, Machine Learning & Predictive Analytics in Banking

Banks in the financial services industry stand at a crossroads: Either carry on, business as usual, or embrace digital transformation and reimagine business operations from top to bottom. One road leads to irrelevance, the other to long-term growth and success. Which one will you choose?

Artificial intelligence (AI), machine learning, and predictive analytics are reshaping the financial services landscape, enabling banks to capitalize on the wealth of customer and product data they possess, gain greater market share, and achieve new heights. Read on to learn more about how these advanced technologies are transforming banking as we know it.

New Customer Acquisition

As customer acquisition costs steadily increase, it’s imperative that banks look for ways to optimize their acquisition efforts in order to reduce spend, starting with lead scoring.

Not all prospective customers are made alike; some will inevitably be a better fit a bank’s products and services than others, making them less likely to churn in the future. These prospects make for ideal leads but can sometimes be difficult to spot. Traditionally, sales teams would thoroughly vet each prospect to determine whether they were a suitable lead, and then qualify those leads. This process, though necessary, is extensive and can significantly slow down new customer acquisition. Now, thanks to predictive analytics in banking, sales teams can use machine learning algorithms to automatically evaluate prospects and prioritize leads based on their likelihood to take action, saving precious time and improving the accuracy of lead qualification.

Now that the sales team has qualified leads, it’s time to pull in the bank’s marketing team for an assist. Marketing is all about reaching the right customer at the right time with the right content; should a bank’s marketing team fail on any of these counts, they risk losing a prospective customer. Banks can use automated segmentation to group leads by their interests and use predictive analytics models, such as response modeling and churn analysis, to determine which marketing content would be most relevant to each of these groups, allowing for a higher degree of personalization.

Understanding Customers

Once a new customer has been onboarded, it’s time to build a detailed customer profile for them based on the information gathered during onboarding. For many years, customer segmentation was achieved by manually sorting customers into discrete groups by demographics; although it was effective enough, this approach was tedious and time-consuming and lacked the nuance of individual customer profiles.

Banks now have the ability to automate customer segmentation through the use of customer relationship management (CRM) technology. Banks can then use the data contained in each customer profile in conjunction with machine learning and predictive analytics to analyze customer behavioral patterns and build predictive models. These models can indicate anything from how likely a particular customer is to visit a physical branch location to what types of marketing they’re most likely to respond to.

Speaking of marketing, machine learning and AI technology is capable not only of indicating which platforms customers prefer to communicate through, but also the type of content they’re most receptive to, which can be incredibly useful when you’re trying to decide whether to target a customer with Facebook ads or an email campaign. Sales teams benefit from predictive analytics in banking, too: Sales teams can use this cutting-edge technology to evaluate the probability of a customer to purchase another product and to identify cross-selling and upselling opportunities, thereby increasing that customer’s lifetime value.

When it comes to customer service, artificial intelligence is king. The banking industry has seen a relatively recent rise in the popularity of customer self-service — a logical trajectory for customer service, given the ubiquity of mobile devices and banking applications. Banks can capitalize on this trend by incorporating AI into their mobile apps, which enables customers to resolve service requests via chatbot. By embracing AI-based chatbot technology, financial institutions can significantly reduce customer service call volume and conserve money and resources in the process.

All of these pieces, taken together, go a long way toward customizing and optimizing the overall banking customer experience and increase the likelihood of customer retention as a result.

Fraud Detection & Prevention

With digital technology come digital threats to security. Fraud is one of the leading causes of cybercrime in the banking industry, with 1.4 million fraud-related cases reported in 2018 alone. Through the use of predictive analytics in banking, financial institutions have been able to identify and monitor potentially fraudulent behavior before it even occurs and to take preventative measures to stop fraud in its tracks.

Banks are able to recognize patterns in customer behavior based on the data they’ve collected over time; they can then apply machine learning to these behavioral patterns to flag anomalous behavior that could be indicative of fraud, thereby notifying a customer service agent to follow up with the customer in question to verify whether fraudulent behavior actually took place.

Risk Management

In the financial services sector, risk takes many forms; in the banking industry, in particular, institutions face cybersecurity risks, compliance risks, operational risks, and more. Banks can leverage predictive analytics to examine historical data, identify previous situations in which risk was mishandled, and get to the heart of what went wrong to get a better understanding of how to more effectively manage risk in the future.

Predictive analytics in banking can also be used to manage risk in real time. Should a bank employee (or other relevant party) engage in behavior that puts the institution at risk, a predictive analytics-based solution can tip the appropriate parties off to the problem and even suggest preventative measures to either mitigate the damage caused by that behavior or eliminate the risk entirely.

Predictive analytics in banking, in all of its forms, isn’t some distant dream — it’s happening here and now, and banks need to update their technology accordingly in order to thrive in an increasingly digitized landscape. Hitachi Solutions can help.

Our Data Science & Machine Learning practice has years of experience leveraging Databricks on the Azure platform to build next generation machine learning pipelines for the financial services industry. Our data scientists have the knowledge and expertise to help you harness the power of artificial intelligence, machine learning, and predictive analytics so that you can future-proof your organization and stay several steps ahead of the competition. To learn more about what Hitachi Solutions can do for you, contact us today.