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How Advanced Data Analytics Can Streamline Retail Banking

Apr. 18, 2017 ⋅ Categories: Branching Topics, Workplace Strategy

Advancements in data analytics are revolutionizing retail banking and banking operations on a scale second only to blockchain. An astronomical amount of data is available to financial institutions today, and within that data are the keys to compliance automation, successful branching strategies, and a better understanding of your customers and the products they need.

Traditional methods of data analysis are barely able to scratch the surface of massive “big data” data sets, but powerful visualization tools and artificial intelligence systems are able to dig deep into large amounts of data to uncover hidden patterns and insights.

The real power behind artificial intelligence is machine learning, a statistical method of “training” software to analyze data and uncover patterns, as well as learning to perform tasks without human instruction.

The training is done with data sets fitting into the three main categories of machine learning, supervised, unsupervised, and reinforcement learning. A supervised machine learning algorithm analyzes a set of inputs and outputs, such as demographics data and product sales, and trains a model to make accurate predictions on new input data. This could predict, for example, how likely a person is to want a mortgage. Unsupervised learning involves analyzing input data without expected outcomes to reveal clusters of similarity, which is useful for initiatives such as market segmentation. Reinforcement learning is a trial and error process where the algorithm starts with a model, often a result of supervised learning, and refines that model based on feedback on its outputs. This is useful for automating processes where a human or machine can look at the output and give feedback on it.

Let’s take a look at a few real world applications of modern data analytics in the financial industry.

Cut Compliance Costs with Automation

Regulatory burden is a hot topic at any CUNA or ABA event, and for smaller financial institutions like credit unions and community or regional banks the biggest impact of regulation is overwhelmingly the labor cost of compliance. 

This is an area where artificial intelligence can have a large impact. Some FinTech firms are already using machine learning to detect activities like fraud and money laundering, and they can even transform the sheer volume of transaction data from a burden into an asset. Each time the software analyzes a transaction, it can use reinforcement learning to refine the model making it even more accurate. Where traditional methods of analyzing this data require significant human attention to maintain and improve accuracy, an artificial intelligence program only needs feedback on its work to improve and adapt.

Numbers aren’t the only data that machines can analyze either. FinTech firms are currently developing natural language processing based tools that process the language of new and existing regulations to extract actionable information and notify relevant employees. Instead of tying up staff time interpreting regulations, you will be able to automatically send employees new compliance policies and enforce them.

With these tools easing the regulatory burden, your staff will be freed up to focus on providing better products and customer service.

Understand your Market to Increase Relevance

The more you understand your market and your customers, the more relevant you can make your financial institution’s marketing and product offerings.

Predictive analytics is a field of data analysis that uses a variety of tools to gain a much more detailed understanding of your target market than traditional market research.

Several years ago Target started using machine learning to find out about customer pregnancies as early as possible. The model they created was so accurate that a surprised father learned his teenage daughter was pregnant when Target sent her a mailer for expecting parents.

Now imagine how much you could increase sales by targeting your customers or individuals in the community with customized financial products at transitional stages in their lives, such as getting their first job or having a baby. There are a number of FinTech firms already offering this service for both account management and digital marketing. And it is possible to customize marketing campaigns to individuals on the internet based on Key Lifestyle Indicators.

By targeting your customers or potential customers individually with customized financial product offerings, a task that would be insurmountable without machine learning, you will be in a unique position offering them exactly what they need, when they need it.

Maximize Branch Network Reach and Optimize Sales Goals

Predictive analytics is also a powerful tool to guide your branch network and sales strategies. Where traditional market research focuses on historical trends, predictive models are able to make accurate predictions of what the demand for financial products and services will be going forward. Predictive models for this application are often refined with supervised machine learning, using training data sets covering vast amounts of demographics and customer behavior data.

Here at Momentum, we use market studies from our data partner Pitney Bowes to help guide our clients’ branch network strategies and design branches that are relevant to their target market. The market study is able to suggest optimum locations down to the neighborhood level, based on the projected branch performance and the network effect. This is a powerful tool when combined with a boots on the ground location selection strategy.

Because predictive models project sales of different product offerings, they are also being used by a number of financial institutions to set sales goals. Setting realistic sales goals that drive performance is a difficult task because goals are often influenced by biases within the organization and expectations that don’t align with the needs and demands of consumers. By taking these biases out of the equation and relying on data, one bank was able to create strategic sales goals that led their team to increase sales by an incredible 73% over three years! This was possible because the sales goals aligned with local demand for banking products.

A Powerful Tool, but Still Just a Tool

Modern data science offers powerful tools that can help your financial institution automate tasks, uncover hidden insight, and guide your strategy. But while they are incredibly useful, it’s important to remember that tools don’t work by themselves. While artificial intelligence is being sold as a catch-all solution to nearly everything these days, it is the organizations and leaders who take the time and effort to learn how to use these tools effectively and what their limitations are that will benefit the most.

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