Want to create (and keep) an ironclad relationship with your valued customers? Let AI lead the way. 

The age of Machine Learning in Finance is just beginning. While many forward-thinking innovators in the banking and financial services sectors have already implemented AI into their organizations through tangible, ROI-driving technologies, most are just scratching the surface of what’s truly possible.

This begs the question: what will the new frontiers of Machine Learning in Finance bring? And how should leaders prepare to stay competitive in their market, keep customers happy, and evolve with smart technologies?

With the limitless (and often unknown) possibilities of AI, it can be tricky for executives to pinpoint where to best invest their time and money. But the answer is simpler than you might think: start by addressing the core concerns of your customers, and prioritize your machine learning roadmap accordingly.

Don’t get us wrong –  investing in AI process automation to reduce clerical errors and lower operational costs is a worthwhile endeavour, especially considering that FinServs can see an estimated 50-70% savings in these contexts, according to Ernst & Young. However, too often, machine learning implementations are disconnected from the end-benefit of the customer.

And yet, today’s Financial Services customer has high expectations for the businesses they work with: nearly half of people aged 23-38 complain that their banking experiences are impersonal, while 70% of consumers believe their relationship with their bank is purely transactional in nature.

At first glance, it might seem counterintuitive to employ AI as a solution for improved customer experience. But AI — and machine learning specifically — can augment your team’s ability to provide top-notch service with a human touch, deepening business-to-consumer relationships and improving overall customer satisfaction.

Share-worthy machine learning in finance statistics:

1. Your clients can receive real-time, tailored recommendations for their financial wellness

Recommender Systems are arguably one of the most widespread business-to-consumer adoptions of AI today. Forward-thinking financial services organizations are stealing a page from the book of powerhouses like Amazon and Netflix to provide relevant, timely product recommendations for their customers.

Recommender Systems analyze historical data, and other relevant data sources to understand the current and future needs of users, successfully generating meaningful and personalized offers and advice based off of rich data sets.

2. Your clients can get personalized individual services when they really need to have it

One of the most interesting ironies in today’s current Machine Learning in Finance landscape is the fact that a huge swath of customers believe they do not have enough human, one-to-one interactions with their bank, and yet, chatbots are one of the most widely adopted uses of AI in banking today.

While chatbots offer great benefits such as immediacy and around-the-clock services, AI prediction systems, on the other hand, can help identify customers at risk of churn. This identification process is possible by analyzing current and historical data to identify patterns and illuminate potential risks. As a result, you can proactively provide smart, human customer service to customers needing extra attention.

3. Your clients can benefit from protection against fraudulent activities 

Every dollar of fraud costs just over $3.00 for financial services companies, a loss that accumulates to 2.79% in lost yearly revenue. And, approximately 1.4 million people are impacted by fraud yearly, which is why fraud protection is the second most important factor when determining customer trust in a financial services.

Anomaly Detection can significantly mitigate this risk. Anomaly Detection is a type of unsupervised machine learning employed to identify unusual patterns, pinpointing outliers that are misaligned with expected behaviors. It has been a proven Machine Learning in Finance technology for its ability to monitor complex multi-parametric systems, highlighting unusual behaviors in real-time.

What’s next for your organization? 

Determining where to start with your AI project is no easy feat. Identifying risks, considering data privacy and ethics, and assessing the impact new technology will have on your current teams and processes can be a lengthy – if not complex – endeavor. But taking your AI strategic planning ‘back to basics’, or in other words, starting with the needs of your customers, will guarantee high ROI and happy customers from your Machine Learning in Finance investments.

Reach out to us to learn more about AI in Financial Services now.