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Retail Banking Trends for 2020 and Beyond

The banking industry faces innovative retail banking trends in 2020 with powerful forces reshaping the sector and creating an imperative for change. Banks and other financial institutions must choose what course of action to take – to either lead the change, follow trends, or manage for the present.

Whatever their strategy of choice is, it’s critical for banks to develop new, innovative solutions by taking advantage of big data, transactional and behavioral analytics, digital technologies, and novel delivery platforms.

From making transactions move faster and smoother to the changing and evolving role of retail banks, it’s not entirely clear what the trends discussed below will mean for the sector and the financial industry as a whole. However, the consensus is that the retail banking trends for 2020 discussed below will favor consumers.

6 Retail Banking Trends for 2020

1 – The Expansion of Open Banking

Many view open banking as a European issue and a threat to traditional business practices – the latter is correct. It refers to any initiatives by banks to open their APIs to third parties, giving them access to the bank’s data and functionality. You can use the term open banking interchangeably with API banking, open APIs, banking-as-a-platform, banking-as-a-service, or ecosystem banking.

The concept for open banking encompasses the need for banks and other lending institutions to respond to consumer pressure for painless and straightforward financial experiences. For instance, to buy homes, transfer and receive payments, or manage their financial lives. Fintechs and other big tech companies have already started leveraging the API banking ecosystem to offer financial services.

2 – Real-Time Financial Products

Banks and consumers alike are driving demand for services and products that they can interact with in real-time. This development will see real-time payments become the expected banking norm in 2020. What’s more, the conversation is sure to shift from how banks can set up for a real-time experience to what they can do to become more competitive and attract clients by leveraging real-time payments.

APIs will play a significant role in real-time growth since the fintech community requires them to interact with the banking services that their customers need. Therefore, retail banking trends for 2020 will focus on setting up new, innovative real-time payment services that attract fintech companies and consumers.

3 – Commitment To Digital Delivery

2020 is already shaping up as the year of enhanced digital banking consumer experiences. The industry is ripe for change thanks to the development of new, incredible technology both within and outside the sector that supports digitalization.

For those still mostly offering traditional banking services, they will shift their primary focus to the integration of new technologies and the enhancement of digital offerings with an emphasis on more value and personalized client experiences.

4 – Always-On “Invisible” Banking

As the business world enters the post-digital age, financial institutions will seamlessly integrate their financial services into the daily lives of consumers. This trend has taken the moniker “invisible banking.” An example of an invisible banking transaction is direct deposits.

Technology has created what experts refer to as an “always-on” world where business opportunities appear and evaporate quickly. A time will come when it will not be enough to have the right products and services, but banks must also recognize the exact moment when consumers need them.

5 – Intelligent Assistants and Voice Banking

Thanks to the rapid consumer adoption of voice and digital assistants, it’s now imperative for banks and other lending institutions to seriously consider the implementation of these services. Statistics support this assertion with a 78% growth of voice assistants and smart speakers users in the U.S.

Already, a handful of large banks have invested in digital assistants, including Capital One, Barclays, BofA, USAA, and U.S. Bank. Some smaller institutions like Mercantile Bank of Michigan have also followed suit.

6 – AI-Driven Predictive Banking

The ability to observe, analyze, interpret, and catalog the actions of your bank customers (while respecting their privacy) allows you to design and deliver rich, individualized experiences that will help build customer loyalty during the post-digital age.

Therefore, the banking industry is leaning towards the consolidation of all internal and external data to build predictive profiles of their customers in real-time.

Banks with a competitive edge in the market will go a step further to help their customers optimize their spending, give them preferred access to excellent deals, and nudge their behaviors in a way that creates a better long-term financial health.

One AI challenge that many institutions face is finding a balance between privacy and proactive insight, which is where transactional and behavioral analytics apply.

The Bottom Line

As the financial services industry undergoes rapid change and retail banking trends in 2020, institutions must invest in transactional and behavioral analysis to remain competitive, increase customer experience, and meet strategic goals.

Since 2002, IFM has been providing clients with cutting edge technological solutions, near real-time insights, predictive machine learning-based intelligence, and behavioral-based triggers. IFM’s proprietary processes is what allows them to provide banks with a data standardization solution and near real-time behavioral insight.

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Cross Selling In Banking: 4 Steps To Predictive Analytics

According to the book, ‘Marketing Metrics’, it is easier to sell a product to an existing customer (a 60-70% conversion rate) than to sell a product to a new qualified prospect (a 5-20% conversion rate). With existing clients, businesses already know their clients’ pain points, and the clients may have already become loyal to the financial institution. In the banking industry, banks often have a variety of products, but a good fraction of current customers might only utilize one or two products.

It can be challenging for a banker to sell a full range of financial products to a single customer, so front-line employees may only master a few of the high-performing financial products. Fortunately, banks have a valuable asset: customer data. With the right approach, a financial institution can evaluate their data and generate insights on cross-selling opportunities. This strategic approach to cross-selling is where predictive analytics comes in.

Here is how predictive analytics can be used for cross selling in banking:

The Power Of Predictive Analytics

The commoditization of financial products makes cross selling in banking arduous. Since customers may feel that they can get a better deal somewhere else, they might pick the most affordable product from your financial institution and hunt for other products elsewhere. This commoditization has resulted in banks bundling multiple banking products in an effort to create higher perceived value for the customer.

However, push-based selling and “one-size-fits-all” campaigns might not suffice to lure the modern-day customer. They need access to valuable products, and they need it now. Any product you bundle with the rest of your financial offerings should add the most value to their lives.

Given that banks collect data through CRM software and online tools, they can use this data to identify what their customers need. The data provides insights into:

  1. What customer to contact first

  2. What to sell them

  3. How to communicate with them

Predictive analysis allows banks to evaluate buyer behavior through recent account activities and sometimes even online activities such as reviews and complaints. Instead of offering a single generalized offer, financial institutions can personalize their products to a specific prospect group which can improve a campaign’s return on investment.

Predictive Analytics Steps

1. Start With A Question

Banks collect vast chunks of data, and they will be nothing more than data without analyzing them. To successfully identify opportunities for cross selling in banking, they must create a question and look for its answers through predictive analysis. Unlike conventional business intelligence (BI) tools that tend to be retrospective in nature, predictive analytics tools should provide insights into the future. You can get answers to questions like:

  • What customer demographics are the most likely to churn?

  • What is the estimated number of leads the institution will get from a marketing campaign?

  • What are the odds of a customer purchasing product y after purchasing product X?

  • How profitable might a specific product package be over the next two years?

2. Collect Data

The next step is to identify and collect the data that might bring the bank close enough to the answers. However, the level of confidence a bank can have in its predictive analytics software will significantly depend on the quality of the data it collects. As long as the data meets a quality threshold, it will provide trustable insights.

For the financial institutions storing outdated, inconsistent, or even incomplete client data in their CRM, data collection can become quite time-consuming. As a result, the onus is upon bank managers to spearhead data quality management that lays the foundation for a streamlined process.

Data stored within banking CRM might not be sufficient for some predictive models. Banks might need to get data from other sources. Some of these sources include:

  • ACH transactions

  • Bill payment behavior

  • Geolocation

  • Personal financial management

  • Wire & check payment data

  • Credit cards and debit cards

3. Build A Predictive Model

Next, data analysts have to create a predictive model that will define and determine the probability of specific events happening. These analysts can leverage artificial intelligence and machine learning methods, such as deep learning or linear regressions, to predict this. Once the model is created, test data has to be used to assess the predictive power of the model. For models that do not meet the expectations of the bank, they can be fine-tuned to offer higher predictive accuracy.

Data normalization can help increase the accuracy of a data model. Normalization helps achieve greater overall database organization, reduction of redundant data, improved data consistency within the database, and makes database security more manageable.

Once an accurate model is created, it can be a game-changer. Managers only need to feed their normalized data into the models and get the output they need to make decisions for cross-selling in banking.

4. Pay Close Attention To Assumptions

The idea that the future will always mirror the past is a major assumption throughout predictive analytics models. While there is some truth to this, consumer behaviors do change with time. If changes occur to the behavioral assumptions you might have made when creating your predictive analysis models; the models can become invalid.

Also, the variables of the models might change with changing market trends or time. For instance, the financial crisis of 2008 was significantly driven to by the assumption that house prices would always go up, which was not the case. Banks should pay attention to these assumptions to ensure that their predictions are still viable.

Predictive analysis isn’t a silver bullet for achieving cross selling in banking. Not all variables can be predicted to come up with trustworthy insights. Everything from the weather to the country’s political landscape can change buyer behavior. However, predictive analytics offers a much better solution for insightfully allocating marketing dollars than running financial marketing campaigns on underdeveloped research and half-baked ideas. Predictive analytics can provide financial institutions with a much-needed competitive advantage.

Reach out to our team at Insight Financial Marketing today to learn how you can get started with predictive analytics and how to translate changes in customer behavior into opportunities for your business.

 

A Quick Guide To Bank Deposit Growth

 

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