5 Ways AI Can Help the Financial Industry
By Neal Munson / August 11, 20213 min read
Artificial intelligence is part of a group of emerging technologies that over the past decade have changed the brand experience financial institutions offer clients. But the tools that create AI are rapidly progressing, and only the financial institutions that use them properly will be able to keep up in the race to the future of finance.
A 2017 survey by Evergage--a cloud-based software--found that 33% of marketers surveyed use AI to deliver personalized web experiences. But, since the onset of the novel coronavirus pandemic, there has been such a large shift in digital consumption that many AI models have been thrown off, forcing companies to retrain their AI algorithms to accurately reflect the changed spending patterns.
If there has ever been a time to catch up in the game, it’s now. Now is the moment when other companies are re-evaluating their AI interactions with society. Now is the time to consider how consumers will rely more on digital AI banking in a post-Covid-19 world.
AI is helping make online banking, digital marketing, social media, and other types of content much more efficient for financial institutions, employees, and clients. Below are five ways AI supports brand experience while helping organizations excel in their business goals:
1. Recommendation Systems
Recommendation systems shape your world. When you type “What to eat in Paris,” the results that make it on your first page are not pulled from a hat: they are tailored for you. A recommendation system is a component of AI that uses data you provide to personalize experiences with the tools you use. The more data the recommendation system has, the better your personalized recommendations will be.
It’s this AI that keeps so many users scrolling on Facebook to see the next niche video, adding that particular one extra item to their Amazon cart before checkout, and discovering their next favorite series to binge watch on Netflix.
In banking, this same AI analyzes, manages, and optimizes paid ad campaigns targeting specific users. This helps banks respond to the needs of clients in a more specific way. It also helps match specific services to specific customers.
2. Fraud and Theft Prevention
Fraud detection and theft prevention capabilities have come a long way as more and more people are using mobile devices. The AI toolbox also offers anomaly detectors: tools for finding the irregular patterns that betray fishy activity.
This form of AI helps prevent fraud and theft. USAA uses AI to monitor client behaviors using apps to analyze patterns of use and detect any anomalies. According to the AI Opportunity Landscape research, approximately 26% of the venture funding raised for AI in the banking industry is for fraud and cybersecurity applications, more than any other use-case category.
But this technology isn't only to protect clients. Emerj, an artificial intelligence market research firm, reports that detecting fraud within banking entities is a necessary capability. Fraud can happen between merchants and issuers with problems like pricing and the omission of unpaid merchandise.
Clearly, there is a need for these tools for finding fraud. However, this area of research is currently less developed. It can be reassuring that your bank has your back. But because the tools are still unrefined, certain non-threatening purchases will be flagged and require inconvenient approval, while larger costly purchases may fly under the radar. For instance, it seemed like overkill for the bank to need to validate my wife’s $40 purchase for blinds from Home Depot, just because it was her first venture into home improvement. A month later, her purchase of a $1000 electric standing desk required no such approval.
The future of detecting suspicious activity will rely not only on customer data, but on better understanding customers themselves to polish off what this tool in AI can offer. This is a ground on which those financial institutions who really succeed will likely get big returns in savings and in customer retention.
3. Improve Customer Journey Personalization
A good customer journey personalization makes for many-a-happy ROI. Boston Consulting Group, a global management consulting firm, estimated in a 2019 report that “for every $100 billion in assets that a bank has, it can achieve as much as $300 million in revenue growth by personalizing its customer interactions.”
You want to be validated and understood, right? Well, you’re not the only one. For that exact reason, people are drawn to organizations that reflect an understanding of their own interests. Within the past couple of years, game-changing techniques in deep learning have been added to the AI toolbox. This development has further expanded the space for everyday natural language to drive personalized systems that have been popular in a wide variety of customer experience domains.
Because the customer journey is more about creating connections rather than creating sales (ironically thus creating sales), improving the journey means improving ways in which a financial institution guides the customer in their personalized needs. AI can help anticipate a customer’s needs through service and information on various channels, such as emailing product recommendations or inviting a customer to apply for a debt loan. This is a good formula for both quality customer experience and a pleasant brand experience.
4. Investment Analysis
AI has helped leave the piles of data, human number crunching, and hours of analysis meetings behind. A decade ago, investors depended on the prowess of traders, but as computing technologies expanded, the human need of finding useful patterns in financial time-series data has grown in parallel. This has made it easier than ever for AI to be integrated with longstanding methods to help financial organizations manage wealth, trade, and finance. There’s also a myriad of laws regulating investments in the US, and AI can help financial institutions meet compliance.
Organizations also need to protect their investments. Banks must follow debt collection compliance, and AI can help banks collect in a more humane way. AI contributes to highly personalized user experiences by reaching out to customers through the channels they prefer. By allowing customers to choose how they want to be reminded of their debt and payments, banks are having higher rates of debt re-payments.
5. Hyperpersonal Chatbots and Customer Service
Chatbots can help financial institutions and customers save time. According to a study from the analyst group, Juniper Research, chatbots can save at least 4 minutes of a customer service agent’s time. Chatbots are connecting clients to the right service while handling simple requests. Many legacy banks serve clients all around the world and chatbots allow for 24/7 service.
Chatbots are becoming more sophisticated through natural language processing (NLP). They’re now more hyperpersonalized and customers are becoming more accustomed to interacting with them.
Hyperpersonalization also eradicates having too many choices for the customer. In banking, when a client needs to make an appointment for a specific type of loan, hyperpersonalization can steer them to the right loan officer and offer their available times. Recent advances in NLP have made big strides toward understanding human emotion through the rapidly growing subfield of sentiment analysis.
Both sentiment analysis and hyperpersonalization are sets of AI capabilities allowing for the automatic detection of the mood of a speaker, making writing tools such as Grammarly possible. These same tools can help a financial institution’s ROI when they are applied to areas such as customer service to automatically assess and analyze the feelings and responses of clients.
This kind of automatic analysis provides a guaranteed way to gather usable information from every customer call and interaction, rather than relying on the seemingly arduous process of customer satisfaction surveys to get the feedback you want.
The tech marketing blog, MarcTech, finds these AI capabilities have an incredible capacity for improving CX:
It’s not just about who the customer is, however, it’s about very rich context, such as the use cases or problems they’re trying to solve, what other products they might be interested in, which country or regulations they operate under, their persona, their stage in the buying cycle, their pain points and other data.
This kind of data analysis can yield a high rate of return on brand experience.
From legacy banks to credit unions, financial institutions are evolving through a digital transformation. Their brand experience is rapidly changing to keep up with the digital demands of consumers in a world surviving through a pandemic.
Codazen works with clients to leverage their AI capabilities as the demands of the digital landscape change. Our team of data scientists find what’s hiding in your organization’s big data. We use Amazon Web Services to secure data and we also format and structure data features to input into an AI model. We embed with your team to work closely and bring creative solutions.
Contact us to learn more about how to maximize your AI capabilities. Codazen can help your institution keep up with the rapid changes of emerging technologies and demands of the market.