Possessing one of the most extensive troves of data, banks have an unprecedented business opportunity to convert this wealth of information into innovative digital solutions internally and for their customers. Artificial intelligence (AI), machine learning (ML), and Big Data harnessed through the lens of banking can deliver newfound customer insights, enhanced financial products and services, strategic competitive edge, and transformative risk management at a level not possible even a few years or months ago.
As these digital technologies evolve, the data underpinning them becomes an increasingly invaluable asset that can set the course for business success (or failure). Impacting all bank functions – front office, treasury, operations, risk, finance, compliance, cybersecurity, etc. – data must be wrapped in a holistic enterprise data strategy to be truly understood and acted on.
Accounting for volatile market conditions, highly regulated environments, and the rapid pace of change, how can bank leaders take charge of their data to make elevated, informed decisions in real-time while avoiding undue risk?
The stories of two organizations help shed some light.
Capital One: A Lesson in Data-Driven Success
Capital One’s data-driven approach included leveraging data analytics and innovative technology to grow into the 12th largest U.S. bank within the 30 years since its inception. One of its most successful strategies was implementing fraud-detection algorithms based on predictive analytics and machine learning. This competitive edge enabled the bank to navigate fluctuations in financial markets over the years while industry peers struggled.
Credit Suisse: A Cautionary Tale
The Credit Suisse collapse in 2023 was a culmination of significant losses and scandals resulting from decades-long failed investments. In one instance, the bank’s relationship to failing Archegos Capital Management in 2021 resulted in a $5 billion loss. While other banks like Goldman Sachs and Morgan Stanley were able to mitigate losses during the same timeframe, it became evident that Credit Suisse’s risk management framework did not have adequate, quality risk and exposure data the bank could access in real-time.
In 2022, Switzerland’s Federal Criminal Court fined Credit Suisse the equivalent of $22 million for failing to prevent money laundering related to a Bulgarian cocaine trafficking gang. With a more robust data infrastructure to support anti-money laundering (AML) monitoring, this event could’ve been avoided.
In these cases, poor data hygiene and practices were fatal.
The Data-Driven Opportunity in Banking
A coherent data strategy is make or break for banks and other financial institutions. As banks continue to generate vast amounts of transactional and customer data, the power of that data can be capitalized on.
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Embracing a well-defined data strategy can, among other things:
- Unlock valuable insights.
- Reduce operational costs and risks.
- Enhance customer experiences.
- Increase revenue.
At its core, a data strategy is a guiding framework that dictates how a business collects, manages, and utilizes data. For banks, this means identifying the critical data, establishing efficient collection and storage processes, and defining strategies for analysis and action. The key lies in data-driven decision-making.
Banks can optimize their relationship to and usage of their data through a sequence of transformative steps:
1. Document a Collective Vision
Building a coalition of key stakeholders is the first step to substantive change. With an assembly of all impacted teams, leaders can define their vision of the future and begin to lay the groundwork for working toward that vision collaboratively.
2. Define Goals and Objectives
Not all metrics are created equally. Determine which specific KPIs matter most to the vision, then develop a strategy for employing data to accomplish these KPIs. Dashboards, often created in Tableau of Power BI, are essential tools for business leaders to monitor progress toward goals in real-time, allowing them to report milestones to other stakeholders, maintain accountability, and build momentum toward mastering the new strategy. Another critical element to achieving success is a holistic data governance framework that establishes controls over every aspect of the organization’s data pipeline. Defining meaningful KPIs goes hand-in-hand with understanding the data governance structure.
3. Collect and Store Data
In an increasingly data-rich environment, knowing which data to collect and store is vital. This often involves leveraging diverse sources, such as customer transaction data, social media data, and external data from economic indicators. The advent of cloud data warehousing has enabled banks to consolidate their data in a single optimized location. However, the challenge lies in seamlessly integrating data from source systems into the data warehouse. That’s where powerful data integration platforms like Fivetran come into play, empowering banks to establish real-time data pipelines that effortlessly migrate data from source systems into the data warehouse. Tools such as Alteryx may be leveraged to supplement the data pipeline in a post-warehouse environment by providing a seamless link between the data warehouse and business intelligence layers.
4. Analyze and Interpret Data
With data collected and stored, the next phase involves uncovering hidden insights. This requires sophisticated tools and techniques, including machine-learning algorithms and advanced analytics. By harnessing these powerful resources, banks can identify patterns and trends that would otherwise remain obscured. For those taking their first steps into the realm of data analysis, leveraging business intelligence tools like Power BI and Tableau can provide actionable insights, automate the analytics process, and enable interactive dashboards.
At the same time, the rapid evolution of GPT-4 and other generative AI platforms has empowered business leaders with a self-service option to break down complex tasks into more achievable objectives. For tasks as simple as updating Python code to selecting a bank’s technology stack, generative AI has multiple options that offer a step-by-step walkthrough of the entire process.
5. Act on Insights
Insights without action are wasted potential. Once you’ve gained valuable insights from data analysis, it’s time to act. This might involve implementing changes to business processes, developing new products or services, or adjusting pricing and marketing strategies. By translating insights into tangible actions, you can drive tangible results and stay ahead of the competition.
6. Monitor and Evaluate
To ensure your data strategy is delivering the desired outcomes, ongoing monitoring and evaluation are essential. Continuously analyze data and make necessary adjustments to your strategy. For banks seeking real-time tracking, automated data pipelines can generate reports with minimal human interaction. This automation streamlines the end-to-end reporting process, saving business users countless development hours. For more mature organizations looking to achieve long-term data success, the Chief Data Officer can play a pivotal role in framing data-related policies and controls. The CDO can work with business leaders across the bank to promote collaboration and achieve data-driven decision-making.
Data-driven enterprises hold a powerful competitive edge. By prioritizing data strategy, banks can harness this edge and deliver unparalleled customer experiences. It all starts with the framework, as outlined above.
To implement a holistic data strategy, contact CrossCountry Consulting.