The buzz around AI is pervasive, compelling financial institutions across all sectors to swiftly adopt its applications. However, given the global economy’s continued volatility and the challenges many companies face with digital transformation, there’s a compelling argument for caution. Moreover, it’s crucial to emphasize that a solid data strategy should precede an AI strategy for every institution. Even advanced AI systems can falter without clean, structured, and relevant data.
In short:
AI is not a panacea for widespread issues like poor data quality.
All financial institutions require clean, reliable, trustworthy data that can be used across an entire organization for many reasons, including modernizing, compliance, and meeting the needs of their customers. Combining bad data with AI tools only sets institutions up for failure. The adage, “garbage in = garbage out,” is a hard reality with AI tools.
Unchecked, AI could worsen matters, leading to a runaway freight train, so every organization should implement a robust data strategy first.
Mind the Digital Gap: Hurdles Remain for Large Financial Firms
Financial services are undergoing a dramatic metamorphosis, driven by rapidly changing customer preferences, tightening regulations, and the ever-evolving technological landscape. The need for a solid data strategy is at the heart of every digital transformation.
Today’s customers have developed a sophisticated palette. Gone are the days of one-dimensional financial transactions; the present and future lie in omnichannel interactions that deliver instant gratification – online, mobile, branch-based, or via call centers. More than a transaction, customers crave personalized experiences. Based on insights from current accounts and online behavior, targeted marketing while respecting privacy preferences is no longer a luxury – it’s a necessity. That’s why financial institutions are in a race to integrate emerging technologies.
The foray into robo-advisors, AI, ML, advanced analytics, and robotic process automation (RPA) promises to enhance various interactions – everything from customer support and money management to loan processing, marketing strategies, and fraud detection.
Simultaneously, global regulations are putting financial institutions in a tighter spot. Stringent rules around privacy, disclosure, fraud prevention, anti-money laundering (AML), and more require a delicate balance.
Most institutions, however, continue to struggle with digital transformation. Outdated software, silos, and a lack of technology talent are just a few reasons. According to McKinsey, only 30 percent of banks that have undergone digital transformation have met their objectives. Most underestimate the complexity of executing a digital transformation. And to understand the imperative of these initiatives, look no further than the amount of fines levied against the industry for various infractions. Global financial institutions were fined a total of $10 billion in the 15 months between September 2018 and December 2019 for compliance violations, according to SalesForce.
The Legacy System Quagmire
Outdated and rigid institutional systems are commonplace across the financial industry. These legacy systems, conceived in the pre-internet “physical branch banking” era, are the Achilles’ heel of financial institutions.
For example, a bank IT application’s average age is 14 years. Applications at digital banks, by comparison, are three years old, according to McKinsey.
While the software has undergone incremental updates, core challenges persist:
- A vast portfolio of legacy applications limits the shift to modern deployment models.
- Original designs falter under the scalability demands of a digital front-end.
- The current systems, burdened with archaic codes like COBOL, are cost-intensive.
- While modernizing the external facets is somewhat feasible, the aging core architecture remains a bottleneck.
Reliable Data is the Missing Link
Data is the critical starting point for any of those projects. Understanding all facets of a customer and their interactions with the institution — and having that full customer view data accessible in real-time – is crucial. In many cases, however, customer data may date back decades.
Much of the data may be siloed in legacy systems or spread across multiple locations that are not easily accessible and readily available. When data is not unified, it potentially puts the financial institution at risk of the customer feeling “They don’t know me.”
Responsiveness is also a concern for bank customers. Even the most loyal customers will lose confidence in banks and financial institutions if questions are not answered quickly.
With the sheer volumes of data financial institutions hold, it is no wonder most are lagging in their data unification efforts. A FI Works study notes that as much as 25% of the data in the average financial institution’s customer information file/customer information is incorrect. All this points to the fact that they are not ready for AI.
The Way Forward: A Data-Centric Strategy
Whether and how AI and machine learning (ML) are reshaping modern enterprises, data remains the prized constant. This is especially true for financial institutions, which produce scads of data. According to a study by Fi Works, the average financial institution has 500 million data elements per $1 billion of assets.
The banking/financial sector relies heavily on management tools to ensure compliance with local, national, and even international data privacy, compliance, and governance regulations, and they must fiercely protect data and customers from cyberattacks and breaches.
Rather than diving into the mammoth task of rewriting core systems, financial institutions should focus on making data from existing applications accessible to contemporary systems. A robust data strategy will pave the way for integrating modern technologies, meeting customer demands, and maintaining a competitive edge in today’s digital age.
The Better Foundation: Unified, Clean Data
Pulling together data from potentially dozens or hundreds of sources is often monumental.
Managing and modernizing data means unifying it from wherever it may exist – from legacy systems in data centers to the cloud and even to the laptop in a branch manager’s or loan officer’s office. Modern master data management (MDM) solutions can help ease the burden.
Modern MDM tools allow the business to see each customer, for example, as a single entity rather than as a series of transactions. The information is pulled from all potential data sources about that customer and then reconciled and free of duplications. These unified records become the “ source of truth” about a customer, allowing businesses to make better-informed decisions about that customer. When implemented, MDM systems integrate all the data systems a company might need to access.
Beyond having access to unified data, teams using the MDM can also automate their interactions and maintain data integrity.
Traditionally, all of this data reconciliation could take years to accomplish. However, pre-packaged solutions have emerged that accelerate the MDM journey to just a few months. Industry-specific MDM comes pre-built with configurations, integrations, and other assets specific to the financial services industry, significantly slashing implementation time. Now, organizations can speed their time-to-value with their data by dramatically reducing the time needed to collect, unify, and enrich their core data. These new solutions can provide other velocity-lifting elements, such as low-code / no-code integration tooling.
With data prepared and cleansed through an MDM solution, financial institutions can tackle AI projects better. With clean, real-time data, banks and other financial institutions can streamline activities, such as customer lifecycle management, conflict-of-interest checks, and reporting on the book of business for each client.
When fueled by MDM, AI/ML, tools can deliver consistent, reliable digital experiences and increase marketing effectiveness by fueling customer-facing systems with accurate, insight-ready data.
They can also improve process efficiency by ensuring users across the institution — such as customer service reps — have consistent, accurate, and complete information in their operational system. Workers can make better and faster operational decisions by promptly activating accurate reporting and analytics. These systems also help speed up integrations during mergers, help institutions manage customer data adhering to consent wishes to retain the trust and avoid fines and comply with regulations to help reduce the potential for financial crimes.
Prepackaged multi-domain MDM models enable financial institutions to get a 360-degree view of customers—their accounts, devices, credit history, loans, and family relationships. By fueling operational and analytics systems with modern MDM, trusted data breaks down data silos within a company, leading to better insights and improved operational outcomes.
AI Needs Clean-Connected Data to Fuel Outcomes
A survey by Workday revealed that 80% of senior leaders deem AI essential for maintaining a competitive edge in their industries. This sentiment echoes the urgency once felt for cloud integration and digital transformation. However, as with those previous movements, merely purchasing and implementing the latest technologies and tools doesn’t guarantee business transformation. A primary obstacle to AI’s broader adoption is the pervasive issue of poor data quality.
A robust data strategy is indispensable for a successful foundation in AI/ML. The quality and trustworthiness of data fed into AI/ML systems are paramount for fostering trust within and outside any organization. AI’s lofty promises to revolutionize business only come to fruition on a foundation of trustworthy data. The fuel-powering enterprise AI must be complete, accurate, and readily available. Modern cloud-native master data management (MDM) closes the trust gap by serving as the central nervous system for your business-critical data. Business models like GenAI, LLMs, and others rely on high-quality, reliable, compliant data updated in real-time.
