By Xav Harrigin

Credit marketing, traditionally the realm of big banks and big fintechs, has been a cornerstone of the financial industry for decades. However, the advent of Big Data and Artificial Intelligence (AI) is reshaping this landscape, offering unprecedented opportunities for personalization, efficiency, and customer satisfaction.

In the past, credit was extended almost exclusively to known customers. Merchants and storekeepers had firsthand knowledge of their customers’ financial conditions, and big banks and fintechs leveraged their extensive customer databases and established reputations to extend credit offers. However, the digital revolution and the rise of Big Data and AI have brought a paradigm shift in this landscape.

Big Data refers to the vast volumes of structured and unstructured data that businesses collect daily. This data can come from various sources, including credit reporting, transaction records, customer interactions, social media, and more. When coupled with AI, these data can provide deep insights into customer behavior, preferences, and financial health, revolutionizing the credit marketing process.

AI, particularly machine learning, plays a pivotal role in making sense of Big Data. It can identify patterns and make predictions far beyond human capabilities, enabling highly personalized marketing strategies. In credit marketing, AI can use predictive modeling to forecast a customer’s likelihood to respond to a particular offer. Further, AI-driven segmentation can group customers based on nuanced behavioral patterns, needs, and preferences.

The true power of Big Data and AI emerges when these technologies intersect. With Big Data providing the raw material and AI the processing power, credit marketers can achieve unprecedented targeting accuracy. The potential of this combination for future credit marketing strategies is immense. For instance, a 2021 study published in the Journal of Enterprise Information Management discusses how the integration of AI and Big Data can capture weak signals in the form of interactions or non-linearities between explanatory variables, yielding prediction improvements over conventional measures of creditworthiness.

However, the use of Big Data and AI in credit marketing is not without challenges. One of the primary concerns is data privacy. As AI systems collect and analyze large quantities of data, ensuring the privacy and security of this data becomes paramount. Businesses and financial institutions in particular must comply with all relevant regulations and ensure that they are transparent about how they collect, use, and store customer data.

Another significant challenge is the risk of algorithmic bias. AI systems learn from the data they are trained on, and if this data contains biases, the AI system can inadvertently perpetuate these biases. This can lead to unfair outcomes in credit marketing, such as certain groups being unfairly targeted or excluded. Therefore, it’s crucial to ensure that the data used to train AI systems is representative and unbiased. A 2022 report by the National Institute of Standards and Technology (NIST) highlights the need for a “socio-technical” approach to mitigating bias in AI, considering not just the technology itself but also its impacts.

As AI and Big Data continue to evolve, they are set to redefine the future of credit marketing. According to a 2021 report by the CFA Institute, we are only in the early stages of the integration of AI, Big Data, and machine learning applications in finance. Banks and fintechs will play a crucial role in shaping this future. A partnership between banks and fintechs can create a symbiotic relationship that leverages the strengths of both. Financial Institutions have the advantage of large customer bases and regulatory experience, while fintechs bring innovation and agility to the table. A 2023 report by the Boston Consulting Group found that as the fintech sector continues to grow, it is estimated to reach $1.5 trillion in annual revenue by 2030, constituting almost 25% of all banking valuations worldwide.

In conclusion, the integration of Big Data and AI in credit marketing is a game-changer. As we move forward, it will be crucial for banks and fintechs to navigate these challenges responsibly together, leveraging the power of these technologies while ensuring data privacy and avoiding algorithmic bias. The future of credit marketing is here, and it’s powered by AI and Big Data.

By Xav Harrigin

In the nascent stages of credit marketing, banks grappled with the formidable task of identifying suitable customers for their credit products. The process was akin to navigating in the dark, with banks casting a wide net with their marketing efforts, hoping to reel in creditworthy borrowers. This approach was not only inefficient but also fraught with risk, as it increased the likelihood of extending credit to individuals who might default on their debts.

This landscape was transformed with the advent of prescreen marketing, a revolutionary strategy that allowed banks to assess the creditworthiness of potential customers before extending credit offers. This proactive approach enabled banks to mitigate risk by focusing their efforts on individuals likely to repay their debts.

Prescreen marketing brought about a fundamental shift in credit marketing. Banks transitioned from a broad, indiscriminate approach to a targeted strategy, focusing their marketing campaigns on a select group of individuals. This not only improved the efficiency and effectiveness of their campaigns but also enhanced customer satisfaction, as individuals received offers tailored to their financial circumstances. The advent of prescreen marketing was a significant milestone in the evolution of credit marketing, paving the way for the data-driven, personalized marketing strategies prevalent today.

Prescreen marketing, a linchpin in the banking industry, is a strategic approach that empowers financial institutions to assess the creditworthiness of potential customers before extending credit offers. This proactive method streamlines the marketing process and mitigates risk, making it an indispensable tool in the banking sector. By harnessing data and predictive analytics, banks can pinpoint suitable candidates for their credit products, bolstering efficiency and profitability. The impact of prescreen marketing on banking is profound; it has revolutionized credit marketing, reshaping how banks engage with customers and the broader market.

Prescreen marketing has been a catalyst for change in the banking industry, offering myriad benefits that have significantly bolstered operational efficiency and profitability. By enabling financial institutions to assess the creditworthiness of potential customers before extending credit offers, prescreen marketing has effectively curtailed the risk of default, thereby bolstering banks’ financial resilience.

Furthermore, prescreen marketing has revolutionized customer targeting strategies. Banks have transitioned from a broad, one-size-fits-all approach to a tailored strategy, customizing their credit offers based on specific customer segments and their credit profiles. This targeted approach not only increases the likelihood of acceptance but also enhances customer satisfaction, fostering improved customer retention rates.

The efficacy of prescreen marketing hinges on its use of data and analytics. Banks leverage a wealth of data, including credit scores, income levels, and payment histories, to predict a customer’s creditworthiness. Advanced analytics tools process this data and generate insights, which inform prescreen marketing strategies. This data-driven approach empowers banks to make informed decisions, optimize their marketing efforts, and ultimately, drive loan growth. As the banking industry continues to evolve, the role of data and analytics in prescreen marketing is set to become even more pivotal.

Prescreen marketing has evolved significantly since its inception, largely driven by advancements in technology. Initially, prescreen marketing was primarily conducted through traditional mail-based campaigns. Banks would send out physical letters to potential customers, offering them pre-approved credit based on their assessed creditworthiness. While effective, this method was time-consuming and resource-intensive.

The advent of digital technology heralded a paradigm shift in prescreen marketing. Banks began to leverage digital platforms to conduct prescreening, enabling them to reach a wider audience more quickly and cost-effectively. Email campaigns, online ads, and mobile notifications became the new norm, offering customers a more convenient and personalized experience.

The role of machine learning and artificial intelligence (AI) in modern prescreen marketing is paramount. These technologies have propelled prescreen marketing to new heights, enabling banks to analyze vast amounts of data with greater accuracy and efficiency. Machine learning algorithms can identify patterns and trends in the data that might elude human analysis, facilitating more precise customer targeting. AI can automate the prescreening process, reducing manual effort and increasing speed. As technology continues to advance, the evolution of prescreen marketing is set to continue, heralding exciting possibilities for the future.

Prescreen marketing has undeniably revolutionized the banking industry, transforming how banks market their credit products. By enabling targeted marketing and risk mitigation, it has significantly enhanced operational efficiency and profitability. The use of data and analytics has been pivotal, facilitating informed decision-making and optimized marketing strategies. Looking ahead, the future of prescreen marketing is promising. With advancements in technology, particularly in machine learning and AI, there is potential for even greater personalization and automation. As banks continue to harness these technologies, prescreen marketing will undoubtedly continue to evolve, further shaping the landscape of credit marketing in the banking industry.

By Xav Harrigin

The Independent Community Bankers of America (ICBA) ThinkTECH Accelerator Demo Day is a nationally acclaimed program that promotes early-stage solutions designed specifically for community banks. The program is known for its commitment to fostering innovation in the banking industry, connecting the most innovative fintech companies with community bankers and industry leaders. At a recent Demo Day, Parker Steed, VP of Sales at Micronotes, delivered a compelling pitch that showcased the company’s innovative, AI-enabled, cloud-based marketing automation solutions for financial institutions (https://www.youtube.com/watch?v=Y_Mt84zq0qI, starts at 39:32).

Steed began the pitch by reflecting on the evolution of banking from in-person branch visits to digital interactions. He shared a personal anecdote about how banking has changed over the years, highlighting the importance of Micronotes’ mission to help community banks maintain strong connections with their customers in an increasingly digital world. He then emphasized the importance of emulating the traditional branch conversations in online and mobile banking environments, a feat made possible by Micronotes.ai’s cutting-edge technology.

Micronotes.ai, based in Boston, serves about 140 community banks in the U.S. The company’s growth has been supported by a distribution relationship with Fiserv and investments from Experian Ventures, TTVCapital, and most recently, Bank Tech Ventures. Being part of the ThinkTECH Accelerator has further enabled Micronotes.ai to connect community banks with their customers using big data, advanced analytics, and engagement technologies.

Steed’s pitch focused on two key areas: deposits and loans. He explained how Micronotes.ai’s technology identifies opportunities for exceptional deposits and initiates conversations with customers to retain those deposits. The company’s AI-driven marketing automation also helps banks predict customer behaviors such as delinquency and attrition, enabling banks to proactively offer solutions like overdraft protection products and retention strategies.

The Micronotes.ai solution goes beyond traditional banner ads in online and mobile banking. It offers 26X the click-through rate (CTR) of banners, more engagement, and most importantly, it starts conversations that enable banks to learn from and about their customers. This approach helps to keep the “community” in community banking.

Steed also highlighted how Micronotes.ai can help uncover “camouflaged small businesses” through the retail banking side of a bank. By identifying these businesses, banks can cross-sell products like small business credit cards or loans without increasing headcount.

The pitch concluded with a demonstration of how Micronotes.ai uses Experian credit data to identify creditworthy customers and find meaningful savings if they were to consolidate their loans with the bank. This personalized approach not only enhances customer experience but also helps banks retain and grow their customer base.

In a world where customers are constantly bombarded by ads from competitors and large fintechs, Micronotes.ai offers a solution that keeps community banks competitive and connected with their customers. It’s not just about retaining and growing deposits or booking better loans; it’s about starting meaningful conversations, developing relationships, and building trust.

Micronotes.ai is revolutionizing the way community banks engage with their customers. By leveraging big data, AI and machine learning, the company is helping banks to better understand their customers, predict their needs, and offer personalized financial solutions. The result is a win-win situation: customers feel understood and valued, while banks increase their revenue and deepen their customer relationships.

If you’re a community bank looking to take your data and make it actionable, Micronotes.ai is ready to help. As Parker Steed concluded in his pitch, “If you need to retain and grow deposits or book better loans, give us a call, we’d be happy to help.”

By Xav Harrigin – Micronotes

The Dawn of AI and Automation in Financial Marketing

In the digital epoch, marketing automation, especially within the data-rich financial services industry, has become a necessity rather than a luxury in 2023, empowering financial institutions to remain competitive. This technology enables teams to convert raw data into actions, allowing finance leaders to critically evaluate business strategies, implement solutions, and cultivate stronger business relationships. Artificial Intelligence (AI), the cornerstone of this transformation, extends beyond its traditional role of executing tasks such as identifying optimal advertising placements. As the driving force behind marketing automation trends in 2023, AI facilitates personalization, a key customer demand. A 2021 McKinsey & Co research substantiates this, revealing that 71% of customers anticipate personalized interactions from companies, with those excelling at personalization generating 40 percent more revenue from these activities than their average counterparts. Thus, AI emerges as the indispensable tool enabling this level of personalization.

The Imperative for Automation in Financial Institution Marketing

In 2023, financial institutions face a myriad of marketing challenges. These include budget constraints, the need for hyper-personalized content marketing, and the shift toward digitalized conversational marketing. Managing large customer databases is a daunting task, with the need to align marketing strategies with customer needs and organizational priorities. Furthermore, the push for personalization has intensified, with customers demanding more than just a name on an email subject line. Balancing digitization with evolving risks and sustainability is another challenge, especially in the wake of the pandemic that accelerated the adoption of digital tools.

However, automation offers a promising solution to these challenges. Marketing automation is a crucial way for companies to save time and money. By automating both complex and simple processes, teams are given time to spend on more critical tasks. According to The Business Research Company, the global marketing automation market size saw a compound annual growth rate of 12.3% between 2022 and 2023. This rise in the adoption rate of automation signifies a shift towards putting certain aspects of strategy on autopilot, allowing for more efficient resource allocation and the ability to focus on other sales strategies.

The Indispensable Role of AI in Automating Financial Institution Marketing

Artificial Intelligence (AI) has become an indispensable tool in analyzing large amounts of data to provide insights and make predictions. As noted by the Harvard Business Review in 2023, AI’s power to gather, analyze, and utilize enormous volumes of individual customer data has been recognized for its ability to achieve precision and scale in personalization. This sentiment is echoed by Persado in 2023, stating that AI-powered marketing tools are infinitely smarter than their predecessors, with traditional marketing tools using human-generated algorithms that tell machines what to do.

AI-powered tools such as chatbots, recommendation systems, and predictive analytics have been instrumental in improving customer service, personalizing marketing campaigns, and optimizing marketing strategies. In the realm of regulatory compliance, AI has been a game-changer. KPMG, in 2023, emphasized the importance of AI in ensuring resilience and vigilance in the face of evolving regulatory landscapes. Thomson Reuters also highlighted in 2023 how AI has helped financial institutions to do more with less, managing the growing volume and breadth of regulation while dealing with new markets, products, and threats created by technology.

The Future of AI in Financial Institution Marketing

In 2023, several emerging trends in AI and automation have been transforming the marketing landscape. Machine learning and natural language processing have been at the forefront of these advancements. Machine learning has been instrumental in analyzing customer behavior and predicting future trends, while natural language processing has been used to understand and respond to customer queries in real-time.

However, the adoption of AI in marketing also comes with potential challenges and ethical considerations. As highlighted by the Harvard Business Review in 2023, generative AI, while popular, comes with a degree of ethical risk. Organizations must prioritize the responsible use of AI to avoid potential pitfalls such as bias, privacy invasion, and misuse of data. Forbes also noted in 2023 that ethical AI has been a concern for many years, and global jurisdictions are finally starting to accelerate AI ethics legislation. This means that businesses must not only focus on leveraging AI for marketing but also ensure they are doing so in an ethical and legally compliant manner.

The Future of Financial Institution Marketing with AI and Automation

The role of AI and automation in financial institution marketing is not just transformative, but revolutionary. AI has the potential to analyze vast amounts of data, provide valuable insights, make accurate predictions, and most importantly, execute on those insights. AI-powered tools such as chatbots, recommendation systems, and conversational marketing automation systems are improving customer service, personalizing marketing campaigns, and optimizing marketing strategies. However, as we embrace these emerging trends in AI and automation, it is imperative for businesses to not only leverage AI for marketing but also ensure they are doing so in an ethical and legally compliant manner. Financial institutions that can effectively harness the power of these technologies while navigating their complexities will be the ones to lead the industry.

In the previous two articles, we’ve discussed strategies for growing deposits from the existing customer/member base. It’s now time to look outside the financial institution for new deposits and beyond traditional advertising practices.

One of the advantages of working with a lot of financial institutions is the ability to overcome data scarcity problems that enable the use of algorithmic techniques that have historically been confined to larger financial institutions with tens of millions of customer records.  For example, a financial institution with 25,000 customers and a 10% annual attrition rate has to spend marketing money to replace 2,500 customers per year.  Let’s further assume that only 20% of those lost customers are profitable, that means that only 500 of those lost customers have desirable characteristics for new account acquisition.  That’s just not a large enough sample to build a robust lookalike audience in most major social media sites (e.g. Meta), which require on the order of 5,000 records to build a robust lookalike audience for target marketing.

Why is the ability to build a lookalike audience important in the quest for new customers?  Answer: Because customer/member acquisition cost and the quality of those acquisitions matter.  Lookalike audiences are important because they allow a desirable audience to be defined using one set of attributes (e.g. deposit balances, credit scores, profitability), and then find a similar and desirable audience using different attributes (e.g. interests, group membership, political affiliation).

These ideas come together into a powerful acquisition strategy when aggregated data sets containing tens of millions of customer deposit records are used to create a seed or source audience from which the major social media platforms can build a robust lookalike audience.

For example, let’s say a financial institution’s operating footprint is 25 counties across 2 states.  And that financial institution wants to acquire deposit customers who demonstrate the capacity to make large deposits into the bank as shown in Figure 1.

Figure 1 – Anomaly large depositors make deposits that are far above their average deposit balance over previous periods.

The financial institution knows who these people are but, unfortunately, there are only 95 of them in the database; far below the threshold to build a robust lookalike audience.  However, for Micronotes, this is a soluble problem because we can find about 10,000 anomaly large depositors in our aggregated client database.  Once those 10,000 records are published as the source or seed audience for the lookalike audience, the remaining work in creating a target audience is to impose the requirement that lookalikes be confined to the two states and 25 counties in which the financial institution operates.  Then, of course, a compelling offer must be made to convert those lookalikes into customers or members.

In future articles, we’ll discuss the performance of lookalike campaigns and how they enable small financial institutions to fully compete with larger financial institutions in new customer/member acquisition.