Data is the lifeblood of the corporate banking industry. Commercial banks, in particular, find themselves at a crossroads where managing corporate data is not just an option—it’s a necessity for survival. Corporate data management in banking is not just about harnessing data for growth and innovation; it’s also about safeguarding, sharing, and maintaining the integrity of critical information.

From securing confidential data to addressing the challenges posed by employee turnover, let’s delve deeper into the intricacies of managing banking-related data.

1. Data Security – Safeguarding the Vaults:

Securing sensitive financial data is paramount. Banks are entrusted with an array of confidential information, from customer account details to transaction records. The onus is on corporate entities to maintain

• state-of-the-art security measures,
• encryption protocols,
• firewall systems, and
• access controls.

These layers of defense are essential to protect against external threats like cyberattacks, which have become increasingly sophisticated.

2. Confidentiality – The Bedrock of Trust:

In the world of finance, trust is non-negotiable. Corporate banking thrives on confidential interactions, and maintaining the privacy of client data is a sacred commitment. Beyond legal requirements, banks must prioritize the ethical duty to safeguard sensitive information. Breaches of confidentiality can lead to severe consequences, eroding trust and incurring significant financial and reputational damage.

3. The Challenge of Data Sharing – Beyond Emails:

In an interconnected financial ecosystem, data sharing is inevitable. Whether it’s communicating with  banks, audit firms, or credit rating agencies, information must flow seamlessly.However, relying on traditional email communication for sharing sensitive data can be a double-edged sword. While convenient, email is vulnerable to data leaks and breaches. Corporations need robust, encrypted channels for data exchange to mitigate these risks.

4. Taming the Data History – The Audit Trail:

The financial sector is subject to rigorous auditing and compliance standards. One of the challenges banks face is managing the history of data effectively, especially concerning audit trails. Regulatory bodies often require comprehensive records of financial transactions and interactions. Ensuring that these audit trails are complete, accurate, and accessible is a demanding task that banks must navigate.

5. The People Factor – Employee Transitions:

Employees are both the custodians and users of banking data. When employees leave or join an organization, there’s a critical handover process. Departing employees must transfer their knowledge, access, and responsibilities seamlessly to their successors. Failure to manage this transition effectively can lead to disruptions, data loss, or unauthorized access.

6. Shared Data Challenges – Collaborating with External Entities:

Modern banking involves collaboration with various external entities. Corporates  often share data with  financial institutions, regulatory bodies, audit firms, credit rating agencies, and more. The challenge in this context is to keep shared data confidential and unaltered while also complying with various regulations.

7. Structured Systems – The Quest for Foolproof Solutions:

Despite the digital advancements in the banking sector, many institutions struggle with the absence of foolproof, structured systems for data management. Handling vast volumes of data, including historical records, in a structured manner is an ongoing challenge. Banks must invest in scalable, data-centric technologies and architectures to address this gap.

A Challenging Landscape

Commercial banks are navigating a landscape marked by shrinking margins, soaring operational costs, and disruptive competition, especially in segments like small and medium-sized businesses (SMBs). The response to these challenges has been a resolute investment in data, advanced analytics, and artificial intelligence (AI). However, the journey towards data-driven transformation is fraught with obstacles.

1. Roadblocks to Data-Driven Mastery
   While many commercial banks have made strides in developing pockets of data and analytics excellence, they struggle to scale these efforts. Scaling up data-driven deployments, embedding data-driven decision-making into daily operations, and using data for transformative change remain elusive goals. The wealth of real-time transactional data available often goes untapped, leaving potential revenue streams untapped.
2. Revenue at Stake
   By underestimating the potential of their data assets, commercial banks risk leaving significant revenue opportunities on the table. Moreover, they may inadvertently create openings for fintechs, e-commerce companies, and other emerging competitors in adjacent markets. These digital-first companies have been quick to leverage data to gain market share and offer innovative solutions that traditional banks are hard-pressed to match.
3. Data-Driven Competition
   New generation banks have built their business models around data. This data-centric approach allows them to tailor their services, create personalized experiences, and make data-driven decisions.However, it’s crucial to note that nationalized banks, with their vast customer bases and resources, have the potential to harness the power of data and catch up in the innovation race. To remain competitive, they should speed up the adoption of data analytics, invest in technology infrastructure, foster a culture of innovation, and prioritize customer-centricity.

By leveraging their strengths and embracing data-driven approaches, nationalized banks can position themselves as formidable contenders in the evolving financial landscape.

Shifting Gears: From Proofs of Concept to Exponential Returns

To thrive in this data-driven landscape, commercial banks must shift their focus from proofs of concept to exponential returns. The era of experimentation is over; tomorrow’s leaders are looking for ways to drive tenfold returns on their data investments. This industry vision starts at the top, with leadership taking ownership of data-driven transformation and making it an enterprise-wide effort.

The Three Obstacles

Three major obstacles stand in the way of commercial banks unlocking the full potential of their data:

1. Organizational Silos: Data and technology often remain confined within product and departmental silos. Collaboration across functional domains is hindered by the lack of processes, systems, and organizational culture to support seamless data sharing.
2. Incremental Improvement: Banks tend to focus on incremental improvements to existing business models and processes. They address immediate pain points rather than pursuing transformative innovations.
3. Digital Transformation Fatigue: After years of digital transformation efforts, banks and their personnel may be fatigued by technology changes, making the prospect of embracing complex IT programs daunting.

Data-Driven Leadership

Data-driven leaders prioritize data on the C-suite agenda, encouraging knowledge sharing, data-driven decision-making, and calculated risks. They view data capabilities not just as competitive differentiators but as vehicles for exponential returns on investment.

Breaking Down the Barriers

Breaking through these obstacles requires a fundamental shift in approach. Data-driven reinvention isn’t limited to specific departments or technology projects—it’s an enterprise-wide endeavor with leadership from the top. Successful data-driven businesses prioritize business value over technology, and they look at how data can drive exponential improvements.

The Role of Data in Commercial Banking

Commercial banks possess a treasure trove of data that can enhance decision-making, empower relationship managers, streamline processes, and deliver added value to customers. This includes first-party and third-party data, as well as “new data” collected from digital interactions and niche data technologies.

Cases for the Data-Driven Commercial Bank

Data-driven insights can empower relationship managers and drive revenue, retention, and cost-reduction strategies in commercial banking.
Here are some practical applications:

• Lead Generation/Prospecting: AI-powered data-driven capabilities can identify high-priority leads for new customer acquisition based on value potential.
• Price Optimization: Banks can identify price sensitivity and calculate optimal pricing for customers.
• Credit and Risk Decisioning: Non-traditional data sources can be used for real-time risk assessment and early warning signals for at-risk customers, preventing non-performing loans.
• Retention: Understanding customers’ context, emerging needs, and satisfaction levels allows banks to improve engagement and satisfaction, reducing attrition.

As banks progress along the data maturity curve, they can use analytics and AI to solve increasingly complex problems and drive higher levels of automation. Starting with smaller-scale applications, such as using machine learning for short-term credit decisions with low risk, they can gradually expand these capabilities to more extensive product lines.

Tying Data Strategy to Corporate Strategy

Banks aiming to transform data into a competitive asset must align their data strategy with their overall corporate strategy. This involves:

• Getting the Basics Right: Ensuring that data and AI support the core business strategy.
• Strategic Partnerships: Collaborating strategically with a select few partners in the ecosystem to provide a comprehensive customer experience.
• Shaping New Business Models: Leveraging data as a competitive differentiator to create entirely new businesses.
• Re-defining Ways of Working: Breaking down functional silos and fostering collaboration between different functions and departments.
• Showing Adaptability: Being agile and ready to adjust course when market conditions or technology necessitate it.
• Continuous Measurement: Establishing a metrics framework to assess quick wins and medium-term goals.

 
Amplifying Data-Driven Strategies

The future of commercial banking lies in amplifying data-driven strategies, not merely implementing them. This means aligning data with corporate strategy and leveraging it to drive higher P/E multiples, which are currently enjoyed by data-driven companies in adjacent industries.

Conclusion

Corporate customer data management in banking is a journey that holds immense potential for growth, innovation, and customer satisfaction. While challenges exist, they can be overcome with visionary leadership, a focus on business value, and a commitment to data-driven excellence. Commercial banks that embark on this journey will not only stay competitive but also lead the way in a data-centric financial landscape. The time for data-driven mastery at scale has arrived, and those who seize the opportunity will reap the rewards.

Vesh