Best practices, Building data pipelines

Is a mid-market banking crisis emerging from the shadows?

The recent collapse of Silicon Valley Bank (SVB) has sent shockwaves through the financial services industry, reminiscent of the great crash of 2008. 

Despite being a significant institution holding over $200 billion in assets and ranking in the Top 20 US wholesale banks, it was relatively unknown to most people outside of the banking or startup sectors, yet its impact will be felt across the world. 

The root causes of the meltdown are still under investigation, along with subsequent interventions from the Federal Government to protect depositors. However, it’s a critical moment for executives and technology leaders in similar-sized organizations to take stock of their own positions and restore trust and confidence in an industry segment that delivers a financial lifeline to a diverse population of retail and small-business customers that are often traditionally underserved by the larger banking conglomerates. 

Challenges for Mid-Market Banks and Credit Unions

Finding themselves in an increasingly competitive market, Federal Credit Unions and mid-market banks face challenges on many fronts. National chains act as safe harbors during times of financial crisis, while online-only banks grow their share of customer wallets as digital technology adoption continues to reach wider markets and demographics.

The pressures of interest rate changes after years of low rates, market volatility and questions around the affordability of financial products and services has made mid-market and regional institutions pause their business-as-usual and look for new approaches. 

Without the financial resources available to the Wall St giants, mid-market banks and credit unions need to take a cost-efficient yet data-savvy strategy approach to ensure profitability – even survival – over the coming years. 

Institutions that prioritize getting to know their customers, optimizing their portfolio of products and personalizing their product offers will develop the data-culture needed to navigate the shockwaves resulting from the failure of banks like SVB – to survive and even thrive in outperforming their competition. 

Building Data Cultures with a Mid-market Budget

Building a data-driven culture in a smaller organization means doing more with less. One of the biggest challenges is to address the landscape of legacy systems, outdated software and manual processes that can impede the ability to analyze data and make swift, informed decisions. In this way, smaller organizations can gain a competitive advantage that leads to increased efficiency, improved customer satisfaction, and ultimately, increased revenue. 

This requires a solid technology foundation that can support high-performance data processing, eliminate bottlenecks or complexity from legacy data pipelines and provide analytics that can bridge the gap between departmental or technical data silos. 

With mid-market banks operating with mid-market budgets, technology investments are generally limited to core banking and back office solutions – keeping the lights on for the “financial business engine” managing the organization’s transactions. 

When it comes to analyzing this data to drive better decisions or optimize performance, solutions are often limited to what comes bundled with the core platforms – with BI or reporting often isolated to specific banking functions rather than offering a unified whole. 

Traditional data warehousing projects in these environments are underwhelming: the end user left with underperforming and often incomplete solutions that can’t answer questions beyond a certain scope or in enough detail to help decision-makers move forward. 

Another challenge for smaller organizations is to shift the mindset away from static, fragile decades-old decision-making processes towards making data-informed, data-literate decisions using all available resources. 

This requires investment and executive commitment to continuous learning and improvement, training employees at all levels in data analytics, visualization and data storytelling to empower them to use data to collaborate better, improve efficiencies and focus on customer-centric innovation to drive growth and loyalty.

With tight constraints on budget and headcount, and with ever-increasing competition on the horizon in an uncertain financial environment, making the most of IT and analytics investment is crucial for long-term survival. 

Maximizing Customer Value: The Role of Customer 360 Analytics in Driving Growth and Profitability

As part of this investment strategy, Customer 360 analytics is foundational for the enterprise, as it helps mid-market banks to better understand and offer services to their customers. By bringing together data from multiple sources, it’s possible to make decisions related to segmentation, churn reduction and customer profitability. 

As organizations mature their analytics, having Customer 360 information as close to real-time as possible is a huge competitive advantage. For finance teams, 360 analysis helps with risk management, identifying potential risks and mitigating them, while improving customer retention through analysis of behavioral patterns and taking proactive measures where necessary. 

Even for bank tellers interacting with customers in the branch network, customer 360 analysis enhances the ability to cross-sell and up-sell products based on a customer’s transaction history and their real-time needs. 

Profitability analysis allows mid-market banks to balance internal-facing revenue objectives against the value returned to customers. Banks and credit unions can use this metric to optimize their financial health, compare branch performance and measure risk-adjusted returns on investments given market conditions. 

The definition of “profitability” is inherently complex and multi-dimensional, with a variety of underlying attributes sourced from multiple back-end data platforms. In legacy data architectures, such definitions are often abstracted away from source systems and presented in pre-filtered or pre-summarized forms that are hard to trace back to original data values. 

Finally, larger financial services organizations have seen dramatic benefits from the delivery of self-service analytics to empower and enable a wider audience of business users and leaders, and these benefits are within reach of smaller firms too. 

Self-service analytics removes the bottlenecks and endless IT requests from business teams to access and work with data. Midsize companies benefit from increased agility in responding to short-term challenges (such as market turbulence or customer activity) or taking advantage of immediate opportunities to upsell or cross new products or services for customers at the point of transaction.

One Midwest-based mutual savings bank with over $2 billion in assets faced challenges around their data analytics maturity with disparate systems and a legacy data warehouse that resulted in underserved customers and slow, inefficient data access for business teams. 

Outdated and rigid core banking systems, along with data latency issues and inconsistency led to poor decision-making that risked both profitability and customer satisfaction: a sure-fire recipe for customer churn to either online or larger competitors. 

The bank’s solution was to build a next-generation data delivery platform using Incorta that integrated with core banking functions, back-office operations systems and external sources such as Google Analytics for engagement metrics. 

After an agile delivery schedule lasting weeks (rather than the traditional months or even years for most data warehouse projects), over 600 users from across the bank gained access to a data platform that transformed the ability to deliver insights on financial performance and help serve customers better, from the CFO down to individual branch tellers. 

Preparing for Turbulent Waters: Using Analytics Technology to Navigate through Uncertainty

In many respects, the challenges imposed on mid-market banks and credit unions are not new. There’s always a need for better access to data and deeper analytics to be able to compete and offer a great customer experience, even when investment budgets are a fraction of the Wall St giants. 

However, recent events, such as the collapse of SVB, highlights both the short-term turbulence in the financial landscape, but also the fragility of the assumptions behind the operating models of even the larger players, such as the risk of movement with federal interest rates and the role of hindsight in critical business decisions. 

To navigate a smooth course through turbulent waters, market-sized organizations must prioritize investments in modern technology platforms that enable data access to 100% of an organization’s data that becomes the analytic foundation for true competitive advantage, including real-time customer 360 and segmented profitability analysis, delivered to the entire organization through modern self-service analytics. 

Perhaps the real mid-market banking crisis emerging from the shadows is the lack of a strong data analytics strategy? 

Failure to act now risks mid-market banks and regional credit unions being left behind or even rendered obsolete as national chains and digital disruptor banks continue their march to penetrate retail and corporate banking. 

Incorta offers an open data delivery platform, delivering powerful analytics solutions that work in real time with 100% of your enterprise data. With high-performance, extensible solutions embedded in the platform, Incorta enables decision-makers to drill confidently through to their most detailed data insights with a fixed consumption model that eliminates traditional data engineering and runtime costs, while complementing existing technology investments. 

Want to learn more? Join us for a live webinar to learn more about how Incorta can help your organization gain a competitive advantage through data analytics, and stay ahead of the curve in an increasingly competitive market.