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How To Use Customer Behavior Analysis in Banking

The banking sector faces a myriad of challenges and opportunities in today’s digital age. Customers are more informed, demanding, and have more choices than ever before. In this competitive and complex environment, banks need to find innovative ways to stand out and meet customer expectations. Insight Financial Marketing (IFM), with its unparalleled expertise in transactional and behavioral analytics, highlights the critical role of customer behavior analysis in achieving these goals. By understanding the nuances of how customers interact with financial products and services, banks can tailor their offerings, improve customer satisfaction, and secure a competitive edge.

Understanding Customer Behavior Analysis

At its core, customer behavior analysis is the meticulous study of customer data to discern patterns, preferences, and tendencies. This analysis spans the entire spectrum of interactions a customer has with a bank, including transaction history, product usage, channel preferences, and responsiveness to marketing initiatives. The goal is to build a comprehensive understanding of the customer that goes beyond superficial metrics, enabling banks to anticipate needs, personalize communication, and ultimately, forge stronger customer relationships.

The Significance of Customer Behavior Analysis in Banking

Customer behavior analysis serves as a linchpin in the banking sector’s shift towards customer-centricity. By delving into the rich data banks already possess, financial institutions can uncover actionable insights that drive strategic decision-making. Whether it’s identifying emerging trends, tailoring financial products, or enhancing customer service, the implications of customer behavior analysis extend across all facets of banking. Moreover, in an era where regulatory compliance and risk management are paramount, understanding customer behavior aids in detecting fraudulent activities and managing credit risks more effectively.

Key Strategies for Implementation

Identifying Customer Segments

Segmentation involves classifying the bank’s diverse customer base into manageable groups with similar behaviors, needs, or characteristics. Advanced analytics and machine learning techniques can enhance segmentation, allowing for dynamic and nuanced categorizations that reflect the real-time financial landscape.

Analyzing Customer Interactions

This strategy entails a comprehensive analysis of customer touchpoints and interactions across all channels. By leveraging data from digital platforms, call centers, and in-person interactions, banks can gain insights into customer preferences and pain points, guiding the optimization of service delivery.

Leveraging Behavioral Data for Marketing Strategies

Behavioral data is a goldmine for crafting personalized marketing campaigns. By understanding the specific needs and behaviors of different segments, banks can design targeted offers that resonate with customers, thereby improving engagement and conversion rates.

Enhancing Customer Journey Maps

Customer journey mapping, enriched with behavioral insights, provides a visual representation of the customer’s experience from initial contact through to long-term loyalty. This tool helps banks identify critical moments of truth and opportunities to delight customers or address pain points effectively.

Benefits of Customer Behavior Analysis

The benefits of customer behavior analysis in banking are multifaceted. Beyond enhancing the customer experience and loyalty, it also contributes to operational efficiencies, cost reduction, and revenue growth. By aligning products and services with customer expectations, banks can improve satisfaction levels, reduce churn, and foster a culture of trust and transparency. Furthermore, customer behavior analysis facilitates better risk management by enabling more accurate credit assessments and fraud detection.

Challenges and Solutions in Customer Behavior Analysis

Adopting analysis of customer behavior is not without its challenges. Issues such as data privacy, data silos, and the need for sophisticated analytical skills can hinder implementation. Overcoming these obstacles requires a strategic approach, including investing in privacy-compliant data management practices, fostering cross-departmental collaboration, and building or acquiring advanced analytics capabilities.

Embracing Behavior Analysis In Financial Services

Embracing customer behavior analysis is imperative for banks aiming to thrive in the digital age. IFM stands ready to support financial institutions in this endeavor, offering the expertise and tools necessary to unlock the full potential of customer insights. By partnering with IFM, banks can embark on a journey towards more personalized, efficient, and secure banking services.

Banks and financial institutions are encouraged to consider customer behavior analysis not as an optional enhancement but as a fundamental component of their strategic planning. The insights derived from analyzing customer behavior can inform every decision, from product development to customer service, setting the stage for sustained success in a rapidly evolving market.

In conclusion, the integration of customer behavior analysis in banking operations is no longer a luxury but a necessity. Financial institutions that prioritize understanding their customers at a granular level will be best positioned to meet the challenges and seize the opportunities of the 21st century.

For more information on how IFM can assist your bank in leveraging customer behavior analysis to its fullest potential, contact us today. Begin the journey to a more insightful, customer-focused banking experience.

 

A Quick Guide To Bank Deposit Growth
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Outlook for the Future: Bank Strategy During COVID-19 and Beyond

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Insight Financial Marketing recently presented a webinar called: Outlook for the Future: Bank Strategy During COVID-19 and Beyond.

We discussed the rapid changes to the economy as well as the impact on the financial situation of consumers and businesses. Now more than ever, financial institutions must have a deep understanding of the financial needs of their consumer and business customers. If your FI has not adopted a strategy to leverage intelligence from your bank’s data to help identify customer behavior trends, it will be more difficult to keep and retain customers because of their rapidly changing needs.

The New York Times recently reported a graphic showing how states are at different stages of reopening across the country. Some are open as normal, some are reopening, some having paused their reopening, and some have reversed and are imposing new social distancing and business closings. The graphic shows how states have proceeded with balancing the spread of COVID-19 by reopening their economies. Even within the footprint of your financial institution, your customers’ financial situation may vary depending on where they live and work.

We also shared insights on how investors have reacted to COVID-19 and economic uncertainty.

Two points here:

    1. Investors have rewarded technology firms as consumers and businesses have shifted their digital behavior.
    2. Retail investors are much more active in the markets due to technology applications that have facilitated free stock trading (driven primarily by the growth of fintech firm Robinhood).

The spending behavior of consumers has been shifting as well. The top earners have dramatically reduced spending while the lowest earners have remained the same. We discussed how the CARES Act has led to a rise in deposits, especially for the largest banks in the country.   In addition, most financial institutions have seen a more modest rise in deposit balances compared to the top 5 largest banks in the US.   The combination of reduced spending by high-income earners, and the influx of stimulus and COVID relief payments from the government will not continue for long. We believe that deposit retention and growth will continue to be important in the years ahead, especially in a low rate environment.

If you are interested in viewing the 30-minute webinar, please let us know. We will gladly provide a link to view the recording.  In the meantime, check out our latest vlog for a snapshot of the webinar content.

 

 

Insight Financial Marketing is an industry leader in helping financial institutions leverage customer insights, detect behavioral changes, and adopt a strategy to better serve the rapidly changing financial needs of its consumer and business customers. To learn more about utilizing our Intelligentsia™ technology please visit our website.

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Use Bank Customer Behavior Data To Determine When People Are Ready To Buy, Borrow, or Refinance

With the fluctuation in the finance industry, the lending environment is rapidly changing for banks, credit unions, and other lending institutions. Historically, banks have been relying on demographic data, including age, education level, gender, race, and geographic location to segment customers. However, the rising younger workforce has rapidly changed this traditional dataset used by banks, pushing them into a new era of customer behavior.

Banks that have adapted to using customer behavior to determine the borrowing habits of their consumers have found new ways to segment customers based on their consumption habits, needs, and preferences. The changing preferences of the young generation, coupled with technological innovations, are paving the way for banks to adopt a strategic approach towards consumer segmentation. Significant drivers are facilitating the transformation of analyzing behavioral data, thus helping lending institutions understand their consumer habits and abilities. These drivers can be categorized into the following areas:

 

Machine Learning

The rapid adoption of machine learning models enables banks to use predictive analytics to detect patterns within big data. This modern approach gives banks the ability to look at their customer’s historical activities to identify which trends would be of most importance to them as compared to relying on demographic data to predict consumption patterns.

Big data has redefined the banking sector to the point where loan opportunities are identifiable through data analytics. Big data and analytics are helping banks locate and target the right people for financial products by analyzing signals based on life events, behavior, and passive information.

Behavior-based signals are some of the concrete actions that consumers take to indicate that they are ready to purchase new financial products. For instance, transactional data can send signals to the bank that there is a potential customer for a mortgage or a loan to purchase an asset. A consumer’s data builds a profile of predictive signals that banks can utilize to provide different financial products.

Digital Services

The explosion of digital services and products that consumers use daily creates an opportunity for banks to acquire data sources to get a better understanding of their customers’ consumption behaviors. This technique is not new in the US, where companies were reported to have spent $20.2 billion acquiring third-party audience data and activation solutions to support their marketing activities. The banking sector will follow a similar approach to segment customers in ways that yield deeper insights, leading to more effective customer service strategies.

Changing Customer Base

Traditionally, banks and other lending institutions succeeded in demographic segmentation due to the customer classification that existed before the eruption of technology. Generation Z and millennials joining the workforce have transformed the banking sector by being socially aware of the technological advancements, which they use for most of their daily functions, including shopping. It is estimated that 61 million of the millennials will join the workforce by 2022, which is an excellent opportunity for banks to take advantage of the tech-savvy customers to sell their products. Given that the younger lot has little patience for brands that do not demonstrate an understanding of their desires and needs, banks will need to do an in-depth analysis of their consumption patterns to appeal to these consumers based on refined and personalized marketing strategies.

How Banks Can Leverage Buyer Persona

1 – Get A Clear Picture Of The Customer

By understanding customer behavior based on their needs, tastes, and preferences, product managers can utilize this information to acquire a higher customer base for specific products. Understanding website behavior, product interest, social media engagement, and email preferences combined with offline activities such as phone calls can help banks leverage digital technology to create products that appeal to their customer base.

2 – Prioritize Personalization

Today’s consumers demand that brands treat them as individuals through personalized marketing. IT executives in financial institutions can help in classifying data based on customer behavior as a way of helping product managers to develop products for a specific consumer niche. Personalizing each product and consumer can be daunting; hence, with the help of Risk Management Executives, the process can be split into categories to be accomplished in bits.

3 – Create A Seamless Customer Journey

Once personalization is complete, the next step is to tie the customer experience together across channels and devices. Banks must leverage in-house transactional data to help in building a continuous journey with customers. The secret is for the entire banking team to keep learning and looking for new ways to apply analytics for fun and profit.

Looking Into The Future

For the future of financial institutions, data will be the greatest asset that these institutions can utilize to build products for their markets. Banks that can combine internal and external data sources to create value will find themselves well adapted to the digital market that will make up for future generations.

For financial institutions, knowing their customers’ actual financial situation is more critical now than ever. Contact our team at IFM to learn more about how we have emerged as the leader in large scale transactional/behavioral analysis for generating detailed knowledge to better understand your customer.

 

A Quick Guide To Bank Deposit Growth

 

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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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Benefits of Big Data in Banking

The banking industry generates an enormous amount of data every day. Some of it comes from ATM logs, ACH transactions, SMS and online banking sessions, voice response systems, and more. Years ago, it wasn’t possible to collect, process, or store massive and complex data sets. Businesses had limited ways, if any, to leverage such data.

Today, there are a variety of technologies that have made big data a pivotal innovation driver in different industries. Big data analytics allows organizations to explore vast data sets to uncover insights like patterns and correlations, customer behavior, market trends, and so forth. This information helps managers to make informed decisions.

Impact of Big Data in Banking

Any financial institution that doesn’t jump onto the big data analytics train will have itself to blame for losing revenue. Studies have shown that the banking sector can attain about 18 percent revenue growth by making use of big data.

According to C-Suite banking executives, the modern customer wants highly personalized services. Big data in banking can help to meet customer demands, grow their business, and improve security and compliance. Here’s how banks can achieve this.

Enhanced Risk Management

Banks utilize business intelligence tools to identify potential risks related to lending money. With big data algorithms, lenders can identify customers with poor credit scores and decide whether to approve or decline their loan application. Big data analytics also assists banks in evaluating market trends and determine the opportune time to raise or lower interest rates for specific clients.

Big data in banking reduces the chance of data entry errors when filling out forms. By analyzing customer data, the system detects anomalies. Similarly, the bank can detect irregular transactions and potential fraud incidents and act accordingly.

For instance, if a person usually makes payments using a credit/debit card, an attempt to withdraw all their funds via ATM should be a matter of concern. It could mean a fraudster is trying to steal from the customer. The bank can call the account holder to clarify if the withdrawal is legitimate. Analyzing transactions using big data analytics has helped banks to ward off many fraudulent actions.

Personalization of Banking Solutions

Clients today detest the traditional one-size-fits-all approach to banking. People want banks that understand their needs and present sensible solutions. Consumers are likely to ignore banks that continually send mismatched offers. Annoyed customers won’t browse the rest of the portfolio, yet it could contain more exciting products.

Insights from big data analytics can help marketers to identify the type of products customers already have and what they would possibly want. They can then target individuals with products and services tailored for them from the point of understanding their needs. By doing this, banks can solve existing problems, win customer loyalty, and differentiate themselves from other financial institutions.

Accurate Cross-Selling

Big data can help banks to cross-sell auxiliary products more effectively by performing predictive analytics using wire data, check data, bill pay data, and credit card/debit card data. To succeed, the organization should focus on the value a product brings and the propensity of an individual to purchase it. A high-saving customer, for example, may be interested in tax-free investment opportunities like mutual funds.

Without information, organizations cannot avoid spamming consumers with unwelcome offers. For instance, it’s not worth the effort to market a short-term loan to a low-spending individual who is struggling with debts.

Banking technology and big data tools such as Hadoop and Fiserv can help automate the job. They can search through large data sets and enable financial institutions to make insightful decisions.

Transaction Channel Identification

Banks can benefit from understanding their customers’ preferred payment channels. Take the example of a business customer who prefers to make payments using paper checks. A business banker can reach out to this client and discuss treasury management service options that could help the customer’s business processes.

Final Thoughts

Businesses that are lagging in the big data analytics race are undoubtedly losing out in many areas. By utilizing big data in banking, banks are winning and retaining customers by offering personalized services and heightening security. Banks, on the other hand, are discovering new business opportunities while making their workplaces more conducive for their staff.

By utilizing big data in banking, banks are winning and retaining customers by offering personalized services by learning more about their customers’ needs. Banks are also discovering new business opportunities while improving risk management.

Insight Financial Marketing has over seventeen years of experience in helping banks identify opportunities to improve customer loyalty, grow revenue, and reduce potential risk through big data processing and analytics.  Contact the IFM team to learn how your institution can begin to reap the benefits of utilizing big data in banking.

 

 

A Quick Guide To Bank Deposit Growth

 

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Using Big Data to Generate Mortgage Leads

Data trails have become an integral part of the modern consumer’s lifestyle. Every day, people leave traces of data on the internet, through bill payments, and even when making phone calls. 90% of the data present in the world today was produced in the last two years. For attentive lenders, these data trails can be a great lead generation tool.

Behind these sets of data sits information that can guide lenders into establishing the risk profiles of potential borrowers as well as unearth new business opportunities. The science lies in identifying the type of data on which to concentrate. The art is determining the kind of insights for which to look. Luckily, with the help of big data analytics tools, machine learning, and the right resources, it can be easy to use such data to revolutionize lead generation and customer retention in the mortgage industry.

Here is how big data can revolutionize lead generation in the mortgage industry:

 

Building the Right First Impression

The customer journey that lenders take potential customers through will have a significant impact on their final decision. Nowadays, digital properties have been playing a pivotal role when it comes to interacting with potential customers, as well as presenting the nitty-gritty details of loan offerings to them. In many cases, the customer’s experience with the company might start with a personalized marketing campaign that drives a prospect to a lender’s website.

If the experience raises some red flags or seems tedious to them, then the chances are that they will look for another business with which to work. For instance, asking customers several random questions only to offer them generic loans might put off some customers. With big data, businesses can analyze both internal and third-party data to come up with a consumer journey that creates the right impression off the bat. The data collected during this experience can also translate into how lenders handle customers throughout the lifetime of their loans, increasing customer retention rates.

Better Assessments

It is quite easy for people with thin credit files to be judged using generic credit scores. In many cases, these people could easily manage to borrow and pay back more than what lenders offer them. Big data can provide insights into the risk profiles of customers who haven’t tapped into enough credit throughout their life. For instance, a good number of millennials might not use credit cards, take out car loans, or even work as salaried employees. This generational behavior makes it unfair to judge such mortgage leads under the generic mortgage models.

However, these people do pay phone bills, own bank accounts, and use a mobile payment app. All of these pieces of data can be significant indicators of their risk profile. This information can produce a more thorough profile that can also apply to underserved communities that lack definitive credit histories.

Detecting Fraud

The mortgage industry is among the most fraud-targeted sectors of the economy. While lenders want to limit fraud as much as possible, they neither want to lose legitimate business nor run afoul with regulators for making aggressive rejections to loan applicants. Luckily, big data analytics can offer the balance for which lenders are looking.

Ideally, big data helps lenders, third-party data suppliers, and FinTech vendors to move past conventional fraud detection methods. These methods involved manual fraud detection processes and siloed data. Proper analysis of big data can limit the number of false positives in fraud detection and identify questionable transactions as soon as they are made. Artificial intelligence can help score the risk profiles of the different transactions against a number of variables. Although these analytics can reduce the cost of relying on conventional detections strategies, they require a complete change in how managers approach risk management.

Increasing Efficiency

Other than controlling costs and improving profit margins, the efficiency at which lenders can handle a loan throughout its entire life will have a significant role to play in how they generate mortgage leads and improve their customer retention rates. Data analytics can have a vital role to play in improving the entire loan application process, enhancing the customer onboarding process, and speeding up loan underwriting. With big data analytics and the consent of the customer, lenders can gain access to consumer data from third-party data providers. These data sources can include banks, employers, and credit bureaus- allowing them to form a better picture of the financials of their mortgage leads.

Machine learning can also be pivotal in preventing last-minute delays in the loan application process by flagging suspicious data points. For instance, if the suspicious activity is that the borrower had made large withdrawals or deposits into their bank account, the system will pick up on this and allow the underwriter or processor to request clarification. The customer can then send their feedback through the analytics application, making it easy to analyze their inherent credit risk.

With this better organized, more comprehensive, and easily searchable data, lenders can rely on the data points to provide high-quality customer credit files. Other than making the underwriting process smooth, these files can provide insights throughout the lifetime of the loan, offering ideas that can improve the experience of a borrower. Lenders can identify ways to improve their loan offerings, respond to customer feedback, and help customers out of tricky situations, all of which can improve their chances of them turning into repeat customers.

Big Data to Generate Mortgage Leads

Big data improves the scope and quality of insights drawn from borrowers’ data. With more emphasis on the analysis of data, lenders can both improve the experience they offer current customers and extend their services beyond the typical client base through the generation of quality mortgage leads. The onus is upon lenders to embrace big data analytics to be part of this remarkable revolution.

Reach out to our team at Insight Financial Marketing today to learn how your business can get started using big data to generate mortgage leads in a way that optimizes the engagement with each customer.

 

A Quick Guide To Bank Deposit Growth

 

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Banking Tech – What is the new role of technology in banking?

A new age of technology exists, and things are moving very fast. Because of this, the banking sector has begun to utilize more of the modern technological advancements. Many of these advancements are leading banks to use data and artificial intelligence (AI) for higher levels of personalization and consumer fulfillment. These trends are only going to grow. Here is a look at the role new technology plays in modern banking.

Keeping Up With The Disrupters

While many in the banking sector were trying to maintain the status quo, disrupters turned up with digital banking and financial solutions utilizing modern banking tech and the mobile devices. These developments forced all banks to reevaluate and start to implement changes to how they service their customers. Now, most banks have applications that work fast and provide services people want their banks to provide.

Mobile technology is playing a significant role for banks, and fine tuning those digital offerings is something into which these banking institutions have put a lot of effort. Artificial intelligence (AI), big data, and predictive analytics are becoming the norm in the banking industry for these reasons.

Bringing Core Services Online

Along with the mobile push comes the push to put core banking services online in general. Banking portals of the past contained heavy limits and often did little to improve a customer’s experience.

Forward-thinking banks have started to utilize modern techniques to attract customers to their financial solutions, and allow customers to make banking decisions quickly and without hassle. Responsive design and a focus on turning technology towards customer service have created vast improvements for the banking industry.

Once again, a lot of this comes from investments in analyzing data and AI. This is especially true of online banking services that can help someone in real-time. By anticipating their questions, banks can provide automated and personalized solutions. Banking tech is assisting financial institutions to do more while saving money in the process.

Creating and Fostering Greater Levels of Personalization

People want to feel like their financial institutions know them, care about them, and are looking out for them. People interact with their banks and bank products all the time and with greater frequency. Each of these interactions is an opportunity for a bank to learn more about their customer.

For example, consider someone who regularly loads funds to their favorite coffee store mobile app every week for a year, from their bank account.  This transaction trend reveals one particular behavioral characteristic about the customer that, along with other data points, present your financial institution with information about the customer’s preferences and lifestyle.  Over time, as new transactions and transaction types are analyzed, and the historical amount of data available to analyze increases, new opportunities to cater to specific customer needs will be identified.

In the near future, a financial institution will be able to anticipate a customer’s needs and gain insight into what the bank can further do to retain that person and other people like them.  Advanced data analytic solutions make it easier for banks to identify trends and make individual suggestions for how to engage customers, that work on a more personal level.

AI, along with advanced data analytic capabilities, help banks learn more about their customers and how to take care of them on a personal level.  The fact that data analysis can create a more human experience for customers is one of those areas in which banks are finding a lot of value in, and is one area that will also create a healthy return on investment into the future.

Data Science and data analytics plays a crucial role for banks at every level and in every department. In this way, technology serves to unify many of the disparate banking departments so the bank can create a better, more secure, and personal experience for each of its customers. Financial Institutions who aren’t doing this, or planning to do this, will find themselves left behind. Reach out to our team at Insight Financial Marketing today to learn how your business can get started with the latest innovative solutions that will increase your bank’s ability to engage each customer with a more personalized experience.

 

A Quick Guide To Bank Deposit Growth

 

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