Tag: finserv

Given the privacy and regulatory issues endemic to the financial services industry (FinServ), it’s taken the industry a bit of time to figure out exactly how to leverage marketing technology (Martech) to reach, retain, and inform its audiences. In line with other sectors like education and retail, FinServ is investing in Martech, and it’s paying off.

What is Martech?

Martech refers to a number of digital tools companies and entrepreneurs deploy to market their products and services to consumers and other stakeholders, from bespoke project management and design software platforms to full-fledged campaign automation and customer relationship management (CRM) platforms. This category includes some of the more well-known digital advertising platforms like Google Ads/Analytics, Microsoft Advertising, Twitter Ads, and YouTube Ads, as well as CRMs such as Salesforce and Microsoft Dynamics. All of these tools serve to streamline digital campaigns, collect and analyze data, enable automated scheduling, reporting and statistics, and produce website heat maps, schedule social media content, and more. Grouped together, these platforms are known as a Martech stack.

How pervasive has Martech become? According to online software marketplace G2, the Martech solutions marketplace has grown 27.8 percent year-over-year, increasing from just over 11,000 solutions in 2023 to more than 14,000 in 2024: this reflects both the evolving complexity of marketing requirements as well as the constant innovation in the space.

If we focus in on FinServ, recent investment activity by major industry players — like Citi, Chase, JPMorgan, and their competitors — underlines the growing importance of Martech stacks in the industry.

  • In March 2024, StarTree, a cloud-based real-time analytics services company powered by Apache Pinot, announced Citi had made a strategic investment in the company. Citi uses the open-source Apache Pinot and StarTree to access trade and risk monitoring for its Markets business.
  • In April 2024, JPMorgan Chase launched Chase Media Solutions, a new digital media business. This bank-led media platform, the first of its kind, enables advertisers to send relevant promotions to around 80 million of the bank’s customers.

Implementing Martech that’s Right on the Money

FinServ’s second-most important currency is, of course, data. Thanks to strategically built and implemented Martech stacks, FinServ companies can work with vast amounts of data to gain insight into the behaviors and preferences of their customers. By adopting a data-driven strategy, these organizations can design individualized marketing campaigns for their specific audiences and deliver them via the most effective channels at the best time. And, yes, AI is a huge contributor to the data-driven results and effectiveness of FinServ Martech stacks, given its ability to analyze and process massive amounts of customer data — effectively democratizing the use of marketing technology.

Implementing Martech that’s right on the money can transform your marketing efforts to produce tangible results, but it’s important to follow the right steps. Similar to the healthcare industry, FinServ is under strict regulations and requirements when it comes to marketing, largely for the protection of investors. There are several laws governing FinServ outreach to consumers, including the Truth in Advertising Act, the Truth in Savings Act and Fair Lending Laws, among others. In December of 2020, the SEC finalized reforms under the Investment Advisers Act to modernize rules governing advertising and marketing by investment firms. Although these rules remain in place today, FinServ organizations are expected to keep up with any changes in the regulatory landscape.

FinServ companies need to invest in the right Martech strategy to be able to follow these laws while still executing marketing campaigns that perform, simultaneously maintaining a seamless ecosystem of interconnected digital marketing tools via strategic integrations to help increase adoption and effective usage. To truly optimize a Martech stack and maximize ROI, these companies need the right roadmap, and the right talent, to get them there. But what is a Martech roadmap, particularly in the FinServ space?

The Martech Roadmap: Your Path to Success

According to Gartner, a Martech roadmap helps CMOs, Marketing Operations Directors, VPs of Strategy and Innovation – and other marketing executives – communicate current and future Martech capabilities to their teams and/or throughout an enterprise. These executives can leverage their roadmaps to plan, compile business and user needs for use-case development, review and acquire new technologies, build fresh capabilities, and predict and adapt to evolving technology, potential risks, and other marketplace disruptions.

What’s the best way to start building a roadmap and begin leveraging everything a Martech stack can do for your FinServ company? You need the right people to help you put the right stack together, experts with real depth in marketing and technology who can provide your organization with the solutions it needs, such as:

  • Platform assessment and implementation
  • Business setups for Adobe Experience Manager (AEM) builds
  • Workfront-to-Workfront project management configuration
  • Integration of Workfront with Adobe Creative Cloud
  • Impeccable, on-brand, user-friendly dashboards
  • Salesforce operations, program, and project management, and administration
  • And so much more…

__________________________________

Bottomline

Martech strategy is, simply put, one of the most important investments a FinServ company — or any organization for that matter — can make. Thinking about putting together a Martech stack that drives measurable ROI and turns your FinServ organization into a formidable competitor? Need a team to activate, maintain, and/or evolve it? One that understands the myriad compliance issues innate to the financial services industry from investment banking and trading services to wealth management and beyond?

At Creative Circle, we have the talent and expertise you need to effectively leverage Martech and stay at its ever-evolving vanguard. We develop tailor-made, talent-driven solutions that meet your organization where it’s at today, and we can build out teams to prepare your brand for tomorrow. In other words, we’ve got Martech expertise and consulting-level support that you can bank on.

We are living in an era of unprecedented tech innovation — one where artificial intelligence, once an elusive sci-fi dream, is increasingly becoming an integral part of our daily existence. Tech companies are touting their latest AI innovations, and soon, AI will likely be woven into the fabric of every platform.

Financial sector companies are always seeking to fast-track insights, improve predictive analytics and forecasting, optimize engagement, and uplevel their overall customer experience. So, naturally, AI’s data-crunching capabilities and lightning-fast analytics sound like the industry’s dream come true.

It’s true there are many compelling use cases for AI by financial services companies in particular, as they strive to differentiate their offerings with technology and deliver increased value by leveraging data-driven insights. We’re talking about everything from improving fraud detection and exploring AI-based assistants as productivity enhancers to optimizing lending parameters and fine-tuning risk assessment.

The question remains: How do they handle this immense potential while operating in such a tightly regulated space?

Real Life Financial Sector AI Use Cases

The financial industry is made up of many subsectors, from banking to fintech, insurance, investments, and more. It’s a highly competitive sector, with companies constantly looking for an edge over one another. Today, leaders in this sector are innovating to bring AI to the forefront of their business.

AI-based lending platforms like Upstart and C3.ai seek to approve more borrowers, lower default rates, and reduce fraud risk. According to the Motley Fool, Upstart uses AI models to screen potential borrowers and establish forecasts on creditworthiness that the company considers to be more accurate than using credit scores.

Where some are focused on creditworthiness, other finance companies are intent on improving fraud detection, which is a severe problem for financial institutions, and companies are looking to AI for new solutions. Machine learning algorithms can sort through vast volumes of transaction data, flagging suspicious and potentially fraudulent activity and recommending risk parameters to help block identity theft attempts, suspicious logins, and fraudulent transactions. IBM’s AI-driven Watson Studio does just that, improving fraud detection, prediction, and prevention for its customers.

Investment platforms, too, are turning to AI to help recommend stock picks and content for users. Robinhood may be the best example of a platform seeking to differentiate itself from competitors by recommending investment opportunities based on things like investing style, history, and risk tolerance, personalizing the user experience, and ramping up engagement.

AI in the World of Finance

AI has historically gained relatively broad adoption in financial services through chatbots and machine learning algorithms, but today’s leaders are setting their sights on deeper applications to supercharge their external offerings as well as enhancing internal operations. Still, finance and other heavily regulated industries like healthcare will always require human judgment. Humans are the most important element of an AI strategy, whether it’s for creative and marketing or financial applications, meaning organizations cannot solely rely on technology to make decisions that significantly impact people’s lives.

The cost-saving potential of AI, however, is not lost on the financial industry, only ramping up the appeal of AI to financial institutions. While companies have made some in-roads to AI adoption in various ways, unique challenges in implementing AI because of compliance concerns and opaque algorithmic processes persist. Here’s how some industry leaders are paving the way, and there are ways to help surmount these hurdles through defined guardrails, transparency, and more.

Top AI Challenges — and Solutions — for Highly Regulated Industries

As highly regulated industries grapple with how to tap into AI’s full potential, it’s important to examine the obstacles that arise. Here are some top challenges — and ways to mitigate them — to implementing this technology that are all too common in highly regulated industries.

1. Challenge: Lack of transparency

Many AI algorithms lack sufficient guardrails and controllability. Their opaque decision-making processes and inner workings make it challenging to detail how a system arrived at a particular output. When it comes to people’s money and health, a lack of transparency simply poses too many risks.

Solution: Provide openness and transparency

AI tools that provide clarity around their conclusions are essential for regulated industries. Consider the training data the model receives, how the system uses the data, how the data is secured, and ensure that a large enough data set is used. This level of openness will allow companies to do audits and ensure the system operates as intended, per regulatory guidelines.

2. Challenge: Risk aversion

Highly regulated industries typically have high-stakes operations, where people’s livelihoods, health, and safety are on the line, which means mistakes come at enormous costs.

Solution: Set expectations

Establish AI’s specific purpose and scope to help align stakeholders and provide a quantifiable framework for measuring success. Ensure there’s ample time to lean into new AI-aided workflows slowly to ensure that they are meeting their mark and pivot if needed.

3. Challenge: Implicit bias and unfairness

AI algorithms actually inherit and even amplify biases in the data sets on which they were trained. While fairness in AI is commonly defined as providing fair or impartial treatment, fair can mean different things in different contexts to different people.

Most AI that is built by outside companies does not give users the ability to detect bias or inherent flaws, which means conclusions can’t be examined or audited. If a doctor uses AI to recommend a course of treatment for one patient, but another patient with a similar diagnosis is given another diagnostic approach, what does this mean? Are they both correct? Wrong? What are the factors that led to divergent recommendations?

Solution: Set guardrails

Whenever possible, control the inputs into your company’s AI platform. By using predefined rules and processes, AI systems are far less likely to deviate from the established norm or violate regulatory policies, improving transparency, enhancing auditability, and trust. Some AI tools have certain guardrails built into their framework to help users avoid non-compliance.

4. Challenge: Hesitancy to change

Finance and healthcare industries often rely on legacy systems, and overhauling them to integrate AI can seem daunting, requiring significant money, time, and resources. Adding to the pressure, any changes must also comply with an ever-changing regulatory landscape that necessitates extensive testing, validation, and documentation.

Solution: Crawl, walk, run

Start small and ramp up slowly. Large-scale adoption before your company is ready could lead to regulatory violations and non-compliance that can set your organization back. Consider the crawl, walk, and run approach as an on-ramp to AI success.

Crawl: Investigate the potential business applications to find low-risk, high-impact functions that can act as test use cases.

Walk: Start with specific cases or departments or apply the tech at just one office or branch. Pressure test how initial forays into using AI are going and learn what is working and what is not.

Run: Once you’ve successfully tested, refined, improved, and verified your company’s selected AI tools, begin to explore integrating it and other AI tools into more complex use cases and larger ecosystems.

__________________________________

Bottomline

AI is sweeping the world — and highly regulated industries like finance and healthcare are still looking for new ways to leverage the power of this technology while remaining compliant with stringent industry regulations. While hurdles exist for more full-throttle implementation, humans remain the most critical element in building and maintaining an AI strategy. Experts must interpret, assess, and verify AI outputs, using critical thinking and judgment to determine the best course of action.

Creative Circle can help your team crawl, walk, and run their way to adopting AI workflows, helping to create seamless and effective interdependencies between your human talent and AI. We have experts ready to get your organization up to speed in carefully calibrated steps so that you, too, can thrive in the fast-evolving world of AI.