Among the more recent technologies, Artificial Intelligence (AI) could have the most significant impact on the financial services industry.

First discovered about 70 years ago, AI has transformed many industries already. From supply chain to retail and travel to education, AI has completely changed how work is done in these industries. The technology is predicted to have a similar impact on finance.

Common Challenges in Finance Marketing

Although financial service providers face many marketing challenges, most providers struggle with three fundamental problems, namely:

Commoditization

As the financial services market grows (thanks mainly to digitization), so does competition. Today the competition is so high that many financial services providers find themselves offering the same products.

Commoditization is a situation where the products and services offered by multiple market players are pretty much the same. When this happens, products from competing players can become interchangeable. As a result, consumers feel that they can move between service providers without losing value. Where there’s high commoditization, it’s very easy to lose customers no matter the quality of your branding.

Lack of Consumer Trust

For a long time now, financial service providers have complained about the lack of trust among clients. In a 2016 survey by the National Association of Retirement Plan Participants, for instance, over 90% of respondents said they did not have faith in their financial services providers.

Again, the chief contributor to the increased distrust is technology. After witnessing so many cyber-attacks and data breaches in the last few years, the majority of consumers feel that their data and money are not safe. The financial crisis of 2008 also seriously eroded the little trust consumers had in financial companies.

Automation

In most of the industries where technology is revolutionizing work, automation is one of the major highlights. In these industries, you’ll find many tasks being automated. You’ll also likely find robotic machines working alongside humans to complete tasks faster and with fewer mistakes.

Unfortunately, the finance industry has lagged in automation for several reasons, one of them being the delicate nature of the landscape. In finance, even one small mistake can have grave and far-reaching consequences. Compliance and regulations also make automation a big headache, often forcing providers to stick to traditional, familiar methods.

How AI Solves the Perennial Challenges

Although it’s impossible to solve all the challenges in finance completely, experts predict that Artificial Intelligence can ease many of the problems. Here’s how;

1 – Smarter Credit Decisions

More than three-quarters of consumers prefer to pay via credit and debit cards. Indeed, only 12% of today’s consumers still prefer to pay in cash. What this means is that the credit card segment is more important to financial institutions than ever.

Artificial intelligence provides for a faster, more accurate assessment of loan candidates – at a lower cost. Better still, AI-powered credit assessment solutions account for a wider variety of factors, leading to better-informed, data-backed decisions.

2 – Risk Management

In financial services markets, risk can be deadly if not given proper attention. Accurate predictions are critical to the protection of businesses.

AI will play a starring role in risk management going forward. Using superfast computers and AI solutions, providers can handle vast amounts of data in a short time. Cognitive computing (a branch of AI) helps to manage both structured and unstructured data, making it possible to catch potential issues early.

3 – Analysis of Customer Behavior

In the financial services industry, institutions find it difficult to develop the same deep relationships with customers that may exist with companies in other industries. Through transactional and behavioral analysis, artificial intelligence is empowering the finance industry with the ability to analyze money movement at scale so F.I’s can anticipate the future financial needs of an individual customer. Service providers such as IFM can work with banks to foster the development of A.I. solutions via IFM’s cutting edge technology that cleans and categorizes bank customer electronic financial transactions in near real-time. IFM’s data analytics service enables financial services firms to offer timely products and services to their clients and strengthens the relationship between a customer and the F.I. With IFM’s capabilities, a financial services firm can analyze client behavior and money movement – in near real-time – and can also trigger security mechanisms if patterns of transaction activity seem unusual.

4 – Personalized Banking

Personalization is the new way to market – even in finance. In multiple studies, consumers have made it clear that they are more likely to buy if the experience is personalized. In one study, for instance, 44% of respondents said they are likely to become repeat customers if a brand offers customized services.

AI currently offers some of the best solutions for personalizing the marketing of financial solutions based on consumer behavior and transactional analysis.

Bottom Line

Financial Services firms are faced with three common marketing challenges: Commoditization of products and services, lack of consumer trust, and the ability to automate solutions. Artificial Intelligence will help to solve these perennial challenges by providing an opportunity for smarter credit decisions, improved risk management, and a more in-depth analysis of customer behavior to provide a more personalized banking experience.

What strategy should your institution move forward with to solve these marketing challenges? Data Science experts believe that the key to developing A.I. solutions that guarantee better productivity and ROI rests on access to clean and categorized transaction data that can be utilized to power A.I. related solutions.

Reach out to our team at Insight Financial Marketing today to learn how IFM’s Intelligentsia™ service could have a positive impact on your institution’s ability to market the financial solutions of the future.

 

 

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.

 

 

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.

 

 

 

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 few decades ago, a simple financial transaction meant putting everything on hold and spending your entire day queuing at a bank. Fast forward to today’s world; you can now deposit, withdraw, and send money in only a few seconds while you go about your business. All this is thanks to modern technology. What’s riveting is that it only gets better. Financial institutions are now taking advantage of the 2.5 quintillion bytes of data created every day to improve service delivery. Through predictive analytics, for instance, banks can achieve a seamless customer experience while at the same time shielding themselves from risks and losses. Here’s what you should know about predictive analytics in financial services. Read more

The digital migration is swiftly revolutionizing the way customers buy products and services. Now that digital banking is used by approximately 51 percent of the world’s adult population, financial institutions should focus on creating a sustainable digital marketing program for a fully digital world. This starts by understanding the applicable digital marketing metrics. The following are the six categories of metrics behind digital marketing for financial services:

1. Traffic Metrics

Traffic metrics are mainly measured and monitored during the traffic generation stage. They are very crucial for both SEO and Pay-Per-Click digital marketing strategies. There are several aspects to consider when evaluating traffic metrics, including site traffic and sources of traffic.

Site Traffic

Significant changes in the overall website traffic can give you an insight into how effective a particular digital marketing strategy is. When evaluating the overall site traffic, you should not only focus on the number of page views or hits your site gets, but you should also consider the number of unique visitors your website gets within a specific period. The more unique visitors your website gets, the higher the probability of acquiring potential customers.

Sources Of Traffic

Identifying where your website traffic is generated from and what specific keywords brought them to you can give you an insight on where you should focus your digital marketing campaigns. If search engines are the primary source of the most traffic, you should focus your efforts on SEO marketing. If most traffic is coming from social networking sites, you should focus more on social media marketing, and so forth. Be sure to explore other traffic sources that may prove to be beneficial for your business.

When evaluating the sources of traffic, it is important to assess both the number of mobile and non-mobile website visitors. As more and more people access the internet through their Internet-capable mobile devices, digital marketers must consider mobile traffic an important metric.

2. Engagement Metrics

Is your website content resonating with your website visitors? After reading your content, do they take any action and, if so, how consistently or regularly? Are website visitors downloading white papers and e-brochures or filling out forms?

There are various ways you can evaluate engagement metrics. One of them is by checking the number of clicks your pay-per-click ads receive. Another way is tracking the number of comments, likes, shares, and reposts on social media. You can utilize Google Analytics to track website and app engagement metrics, including page views, unique visitors, and the average time spent on your content.

To boost engagement, you should consider including at least one call-to-action on each of your landing pages, services pages, email, or any other marketing channels that presents a conversion opportunity. You should also review all of your communication channels so you can identify the ones that are generating your desired response. By doing so, you will be able to determine what to change and what to replicate in future digital marketing campaigns.

3. Retention Metrics

Retention metrics are all about establishing whether you are holding your prospects and customers’ attention beyond the initial contact. You should not only check the number of returning website visitors and social media followers, but you should also take note of the bounce-rate, opt-out rate, and the number of unsubscribes.

If the retention numbers outnumber the opt-outs, it’s a good sign that your marketing message is resonating with the target audience. If the retention numbers are decreasing, you should revise your messaging and align it with your target audience’s needs.

4. Conversion Metrics

While getting lots of traffic to your website is an achievement, it won’t mean much if your site visitors remain just that – visitors. The primary purpose of your digital marketing campaign is to convert website traffic into potential customers. As a financial institution, the conversion metrics you should pay attention to are the number of new account openings and new loan applications you get after launching your digital marketing campaigns.

5. Revenue Metrics

The success of your digital marketing campaign can be evaluated appropriately by revenue metrics. You can determine the Return On Investment (ROI) by assessing the website traffic that eventually converted into new business leads or paying customers. By evaluating this metric, you will be able to identify the areas in your digital marketing campaign that are driving sales and revenue.

6. Cost Metrics

This is where you evaluate the amount you spend to launch your marketing campaigns. You have to consider metrics such as the amount you spend on every direct mail campaign you make, every monthly blog post or newsletter you publish, etc. Be sure to determine how each of your marketing efforts is impacting the bottom line, and then use your findings to plan a viable strategy for future digital marketing campaigns and sales cycles.

Data Is An Asset

Everyone agrees that data is one of the most valuable assets any business can have. It’s not the data itself that matters, but what a company does with it. With lots of data at hand, financial institutions have to rethink the way they handle data to be more customer-centric, and, as a result, more profitable.

 

 

Preach the value of your product and the customer will come to you; this is the conventional mindset through which traditional marketers in the financial sector used to attract customers. Having a billboard to talk about how cheap a product is and commercial portraying an optimized customer journey would be enough, but times have changed. The modern-day customers have shifted their mentality towards being attracted to more than just the price of financial solutions.

On top of getting something affordable, they want better data protection, faster service delivery, and even better customer support. With issues such as data theft and fraud turning into a regular occurrence, the need to weave these aspects into marketing strategy becomes even more apparent. Even worse, more companies are cropping up with better solutions every day, which are easily accessible.

Digitization of the financial industry does little to make the life of the traditional bank executive easier. The most successful modern bank executives view this disruption as an opportunity rather than a threat. It takes the concerted effort of the whole organization (marketing, HR, finance and analytics department, etc.) to prepare a bank for the changing landscape of the modern financial industry.

Here are some things that should be done to attract the attention of modern financial customers:

It Starts With Changing the Marketing Mentality

Sure, the time to shine might have already expired for the traditional marketing models, but the modern-day ones can’t exist without these models. The 7Ps (price, product, promotion, place, people, physical, and process) still play a significant role in identifying the best way to direct marketing efforts- though they were majorly used in the 1990s. On the flip side, calculating ROI and using the 4Cs (customer, convenience, communication, and cost) brings a new perspective into marketing.

Ideally, blending both the modern and traditional models should help understand more about the brand, analytics, customer insights, and customer experience. This combination also helps to identify capability gaps in the choice of marketing strategy and looking for ways to bridge the gap.

Data Should Be Treated As an Asset

Consumer and product data paints a picture of who the target consumer is. It provides details about the buyer personas in the line of what makes them tick and itch. As a result, marketing teams need to encourage improving the sources of data to make their firm more competent to provide for the market.

This goal will need them to invest in data analytics tools that can help track the customer journey. Since every detail counts, working in collaboration with the analytics team is crucial. What’s even more critical is the protection of the collected data from both cybercriminals and the competition- to earn the trust of customers.

Collaboration With Bank Executives

Bank leadership often view the marketing function as merely a cost center. This perspective can accompany some form of distrust by the CEOs, especially if they fail to see a measurable return on investment. Fifty-seven percent of current CMOs have only held on to their position in the firm for three years and less, according to the Harvard Business Review research. This situation breeds a problem where top bank executives might subtly ignore great ideas from marketers.

The best way to bridge this gap of ideology is for bank marketing executives to work hand in hand with other bank executives. Bank marketing executives should turn themselves into ROI marketers seeking to provide value for each marketing dollar spent. Additionally, they should make it their goal to use cost-effective marketing methods to grab the attention of other bank executives. By understanding where the other bank executives are coming from, it will become quite easy to craft ideas into those that can rhyme with their expectations.

It Takes the Right Talent Pool

While modern marketing techniques and data-driven insight will get an organization the right customers, it takes a unique talent pool to put them into action. Everyone on a modern bank marketing team, including marketing managers, big data scientists, and the analytics team needs on the same page in regards to the bank’s marketing goals and objectives.

They need to be fluent in using analytics and artificial intelligence technology to place the business favorably in the market. This means getting the right data, working on personalized financial solutions, and measuring marketing effectiveness. With a best-practice strategy for modern bank marketing, identifying innovations that will make bank marketing more targeted and efficient becomes easier.

The Whole Organization Should Be Involved

Sixty-six percent of surveyed financial institutions are of the idea that having a consistent marketing message throughout the organization is pivotal for success. It can be baffling for a customer to talk to the sales team and the customer service team while receiving a different message from both. Ideally, you should ensure that all departments work in collaboration in supporting the marketing message that you create for your financial solutions.

Because it only takes one broken link to lose a customer, the marketing team should ensure that all departments and outside providers are more than aware of the role they play in the marketing strategy. They should also request bank executives to provide the tools and technology to make this collaboration easier.

Disruption in the financial industry isn’t going to end today, and only the most resourceful marketing teams can best place their financial solutions close enough to the consumer. It takes an overhaul from the conventional marketing mentality to remain viable in today’s marketing atmosphere. The more banks can learn to change their internal and external mindset, the easier it will be to attract the right customers.

 

 

Artificial intelligence is slowly taking over daily business operations. Many different industries are using artificial intelligence to improve efficiency and enhance the customer experience. From automating repetitive tasks to analyzing large amounts of data in real time, the collection of technologies that comprise artificial intelligence hold much promise for the future.

However, your bank may be lagging in adopting AI. While many other industries are using AI to add value to their overall business strategy, banks are still struggling to develop processes that link distinct data sets while keeping confidential data safe.

This slow adoption doesn’t mean that AI technologies are inapplicable to banking. AI is capable of saving the banking sector over $1 trillion in the next 12 years. Subsets of AI, such as machine learning and language processing, can be used to enhance the online banking experience, improve data security, and automate mundane tasks for employees.

Use Cases for AI in the banking sector

The use cases for AI in banks are widespread. While many of these technologies may currently be in their early stages, growth and usability will increase at a rapid pace over the next few years.

Here are five use cases of AI in banks.

1 – Using intelligent analytics to initiate real-time data analysis

As with many other industries, banks need to follow a data-driven approach if they wish to remain competitive. Data in banking is widespread- including financial records, customer data, market data, ACH data, and credit reports. Artificial intelligence can be used in enabling banks to receive analysis from these data sources more efficiently.

Rather than merely using descriptive analytics, AI allows banks to uncover valuable insights in their data through predictive and prescriptive analytics. This means that you can use complex algorithms and tools that sift through large quantities of data and uncover patterns/correlations that you didn’t think existed before.

These new insights can then be used to model investment risk, implement biometric security models, and detect fraud in daily financial transactions.

2 – Using predictive analytics to enhance data safety

Your bank can also use AI technologies to enhance the safety of customer data. For example, machine learning can be used to implement new security measures that are harder to bypass. Voice recognition is currently being applied to assist with password protection, while predictive analytics can be used to detect unusual customer behavior and alert relevant personnel in good time.

Geographical controls are also useful, where transactions made from a different area than usual can be flagged and verified before approval.

3 – Using digital personal assistants to enhance the customer experience

Digital personal assistants (also called chatbots) are designed to interact with customers in a more human-like behavior. Think of it as a 24/7 personalized customer service resource, which your customers can use to request for information, assistance, and advice. Some chatbots even deliver timely financial tips to bank customers via voice and text.

In this way, your employees will spend less time addressing basic requests (such as balance inquiries and transferring funds) and more time attending to complex customer concerns (such as resolving fraudulent transactions).

4 – Enhance the user interface of mobile banking apps

Online and mobile banking has become the norm for customers today. Many account holders don’t make trips to their nearest bank branch unless they have to.

Artificial intelligence can be used to enhance both the mobile and online banking experience by providing personal, secure, and convenient services. For example, machine learning tracks user behavior and offers a wide range of personalized suggestions.

From budgeting tips to personal planning assistance, the user interfaces of mobile banking apps can be enhanced by AI technologies. This improved experience can be achieved at a lower cost and without increasing the workload of employees.

5 – Using Robotic Process Automation to carry out repetitive tasks

RPA (Robotic Process Automation) is a type of AI technology that can be used to automate repetitive human tasks in a more accurate and timely manner. What happens is that various inputs are set, after which specific rules can be applied to those inputs.

RPA can be used to examine loan applications, aggregate data into a common database, and even automate basic procurement processes (such as purchasing office supplies).

Implementing RPA allows banks to reduce human error, carry out mundane processes faster, and make better decisions based on analytics.

Getting Started With AI

With the numerous benefits that AI provides to banks, you shouldn’t be left behind in implementing these technologies. Don’t know where to start? Insight Financial Marketing (IFM) provides AI support to banks in many different ways. Our real-time customer behavior intelligence technologies help you uncover valuable insights and improve the overall user experience.

IFM also has proprietary Artificial Intelligence solutions that can be leveraged to make your bank more competitive in a fast-moving world. Ready to take the next step into intelligent analytics? Contact IFM today.

 

 

The ACH Network has seen significant increases in its utilization by both businesses and consumers. Learning how to harness this information real time will help your bank stay ahead of the competition. In 2018, the adoption of electronic payments reached new heights with an increase of over 1.5 billion additional payments registered with the NACHA service (the electronic payments association that implements and oversees ACH transactions). Although this increase in ACH transactions has been a trend for the past half-decade, the addition of more than 1 billion payments is still impressive.

ACH Transaction: The Numbers Breakdown

The following is a more in-depth breakdown of the numbers concerning the success of ACH transactions of late.

  • The total transaction volume of 23 billion payments seen in the year 2018 was a nearly 7% increase over the previous year, and the highest one seen in the past decade (since 2008).
  • These 23 billion payments translated to an impressive $51.2 trillion, which is about equal to the combined gross domestic products of the top three nations on Earth – the United States, the European Union, and China.
  • Although the payment tally has been increasing consistently for the last six years, this total for 2018 still represented an unexpected boom and far outpaced the projected values.

Diving Into the Details of ACH Transactions

To get a better idea of the significance of this gigantic increase in this particular form of electronic payment, and how it stacks up against the alternatives, consider the volume of same-day ACH payments. With 178 million same-day payments in 2018, this represented an increase of a 137% when compared to the previous year:

2017: 130 million same-day ACH transactions
2018: 178 million same-day ACH transactions

The total amount from 2018 ACH transactions was nearly $160 billion, an 83% increase in the numbers from the year 2017. As remarkable as the above numbers are, the economic character of ACH transactions is slated to continue improving as NACHA is focused on the effectiveness of the Same Day ACH dimension to enhance its benefits to business and consumers.

The Impact of this Growing Method of Electronic Payments on a Business

As the systems behind ACH transaction payments become more robust, you can expect the analysis of consumer financial behavior to become increasingly prevalent. After all, there is already an increased competition between payment and debit systems. Financial institutions that can predict changes in consumer behavior are primed to pull ahead of their competition.

The impact of consumer behavior becomes especially important to a financial institution as it grows in size, requiring the financial institution to identify trends in consumer behavior data. Proper strategic decision making based on insight born from transactional data will see the size of the deposits grow for a financial institution.

NACHA: Major Points of Interest

  • Although an emphasis on expediting and increasing the volume of same-day payments is the future of the ACH transaction, same-day payments comprised a modest percentage of the overall transactions in 2018.
  • Same-day ACH payments were still a 137% improvement over the previous year. It is precisely this unprecedented level of growth that has NACHA placing such an emphasis on further improving same-day payments.
  • Both Business-to-Business (B2B) and Business-to-Consumer (B2C) transactions experienced significant increases in the realm of digital commerce; notably, the preference for electronic means of payment over paper checks. For B2B, the increase was 9.4%, and for Person to Person (P2P), the number was a 32% increase.
  • Overall, many more transactions occurred in the online space than the previous year, with a 14.2% increase.
  • Direct deposits from employer to the bank account of an employee also saw a 4.4% rise, which is expected to grow considerably as more and more banks offer incentives for consumers to opt for this transactional method.

Conclusion

The increase in ACH transaction numbers are staggering and only expected to increase. With total business (B2B) transactions in the range of $35 trillion in 2018, the opportunity to analyze and make proper decisions based on ACH transaction data could mean the difference between close competitors in the financial services industry.