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September 23, 2016

The Past, Present and Future of Finance

Lori Martel

(Stuart Miles/Shutterstock)

Until recently, the personal finance industry was stuck in the past. Consumers could only capture data and insights on spending that had already occurred. Today, people are creating and given access to more data than ever from just about every aspect of their lives – including bank, insurance, healthcare, education, and legal data. Transaction data from various sources can be leveraged in new and innovative ways to help consumers better understand and take ownership of their financial lives.

In an effort to stay competitive, many companies across industries are pushing to make their data work harder for them by trying to use it in a way that provides meaningful insights to customers. Applying this to the finance industry reveals a significant opportunity for enabling a comprehensive, 360-degree profile that considers the past, present and future of a person’s financial life and allows people to make more informed decisions.

To start building this all-inclusive financial profile, it’s essential to realize that a given consumer distributes and spends their money across a wide variety of institutions, and this data lives in innumerable, disparate places. Collecting all of this data can create a foundation to start building a consumer’s true financial profile, but without a mechanism that transforms the data into a common format for processing while providing relevant and timely insight, financial institutions cannot accurately understand and predict one’s financial wellness.

Customer 360

From the financial institution’s perspective, understanding context of how transaction data is categorized, such as location, time of transaction, and more are invaluable when trying to evaluate a person’s financial profile.customer_data_buttons

However, the merchant name associated with a transaction does not by itself reveal a customer’s spending behavior. For example, if a customer had a transaction at Target, today’s big data analysis solutions would tag that data point as “general merchandise,” but that doesn’t provide any context and may not accurately capture what happened. In reality, the person could be making a purchase, requesting a refund, making a payment on their Target RedCard, or even receiving a paycheck from their employer. Simply identifying where a transaction is made is not enough; the value resides in finding out what people are actually doing.

Achieving greater context and determining relevance is the only way that the financial industry can shift toward making more intelligent and insightful recommendations to its customers based on their financial history.

From the consumer’s viewpoint, they are overwhelmed by their finances, and while everyone would like greater understanding and control over their financial wellness, the complexity and confusion of trying to analyze it prevents consumers from getting a clear picture of where they stand. Financial institutions are working to simplify the relationship consumers have with their financial data, relieve the stress and give them more control.

Role of Machine Learning

Additionally, a consumer’s relationship to their finances is just as much emotional as it is numbers and figures, so it’s critical to ensure that the data is as relevant and accurate as possible so it can be understood and trusted by the average consumer. If someone has to spend time sorting through all of their transactions and retagging those that were incorrectly categorized due to lack of context, it drives the consumer away from trusting – and using – financial apps.

machine learning_2To improve the consumer experience, financial institutions can leverage machine learning to understand patterns of transactions and provide cleaner, more readable transactions – which is essential for mobile and other digital banking services.

Ultimately, machine learning is playing a big part in achieving both greater context and improved financial wellness in the financial industry by better tracking transaction data and automatically identifying patterns. With the help of machine learning, financial institutions can start to recognize reoccurring spending habits and make projections for the future. Among other things, these predictions can help people save toward financial goals by making informed recommendations, such as future investment plans and emergency savings opportunities required to feel financially stable.

If the finance industry enables this 360-degree financial profile, it will make the effort consumers put into understanding how they spend, how they save, and eventually how they invest, worthwhile. This will lead to better recommendations and guidance while providing a truly easy, relevant and insightful experience that benefits the customer’s financial wellness and maintains their trust with their banks.

Looking forward, people will take comfort knowing they are financially sound and in control of their financial wellness through the more advanced and intelligent use of their data. While technology is evolving the way financial organizations approach customers, it’s still important to keep the human aspects present. Consumers still need to feel like they’re having a personal experience and that their financial data is being handled with great care. By providing a clear picture of where they stand with their finances, they gain trust that they are making the most informed decisions and feel confident in their financial lives. 

About the author: Lori Martel is the vice president of platform product lori-martelmanagement at Envestnet | Yodlee, a data aggregation and data analytics platform provider, where she oversees product plans pertaining to the aggregation, enrichment, and API-based access to financial data across a network of more than 14,500 data sources.  Lori is driving strategic initiatives focused on leveraging financial data for financial wellness, wealth management, credit, and payments based solutions. Her background includes over 18 years of financial services experience centered around customer engagement, relationship management, and master data management at companies including Oracle, Epiphany, Inc. and KMPG Consulting.