Data Modernization: Banks’ Opportunity to Leverage Regulation for Innovation
Regulatory change is not a novel concept in the banking and financial services (BFS) industry. However, what’s novel is the significant change in the level of data granularity required, along with the frequency and approach for complying with regulatory requirements.
Several new guidelines and regulations are being implemented, along with cloud adoption and digitization schemes building scope within BFS companies enforce organizations to take a new look at how they handle and leverage their data. Hence, signifying the implementation of Data Modernization in Banks.
Because data modernization is not about spending in schemes to sustain a banking license- it’s about minimizing risk, reducing cost, and knowing opportunities to create effective products and services for customers and to drive innovation.
A Look at Modern Data Modernization for Banks
What data modernization refers to in risk and compliance is the delivery and design of operating models and system architectures that smoothens the end-to-end procedure of sharing, processing, and collecting data which increases efficiency and allows room for innovation.
Modern data modernization must process technical or organizational guidelines information that can be deposited, evaluated, and shared in several forms for compliance reporting.
An eminent data modernization lifecycle includes a set of modern data architecture elements, specifically:
1. Data Fabric
Data fabric is an architecture or system that enables organizations to integrate and manage data from multiple sources across hybrid and multi-cloud environments. It provides a unified, consistent view of data, allowing businesses to access and use data in real-time, regardless of its location or format.
Data fabric solutions typically include features such as data integration, data transformation, data quality, data governance, and data security to ensure that data is accurate, trustworthy, and secure. The goal of data fabric is to provide a single, unified view of an organization’s data, making it easier for users to access, analyze, and gain insights from data.
2. DataOps
DataOps involves applying agile and DevOps methodologies to the data pipeline, from data ingestion and processing to data delivery and consumption. By adopting DataOps practices, organizations can improve the quality, agility, and reliability of their data pipelines, and ultimately, drive better business outcomes.
Key principles of DataOps include continuous integration and delivery of data pipelines, version control of data artifacts, automated testing and validation, and monitoring and observability of data processes.
The goal of DataOps is to accelerate the time-to-value of data by improving collaboration, automation, and feedback loops between cross-functional teams, such as data engineers, data scientists, and business analysts.
3. Data Responsibility
Data responsibility refers to the ethical and accountable management of data by individuals, organizations, and governments. It includes principles and practices of data privacy, security, transparency, and governance, and emphasizes the need to use data in a way that respects the rights and interests of individuals and society.
It ensures that individuals have control over their data and are informed about how their data is being used. It also involves taking responsibility for any harm that may result from the use of data and taking steps to mitigate risks associated with data breaches or misuse.
4. Enterprise AI
“Enterprise AI” refers to the use of artificial intelligence (AI) technologies within large organizations to improve business operations, decision-making, and outcomes. This can involve using AI to automate repetitive tasks, identify patterns and insights in large data sets, enhance customer experiences, optimize supply chain management, improve fraud detection and cybersecurity, and more.
It often involves the use of machine learning algorithms, natural language processing, computer vision, and other advanced AI techniques to derive insights and make predictions based on large and complex data sets.
5. BizOps
“BizOps” is a business operations function that focuses on optimizing business processes and improving organizational efficiency. BizOps teams typically work to identify and eliminate inefficiencies and facilitate cross-functional collaboration to improve decision-making and drive growth.
The BizOps function sits at the intersection of business strategy and operations and involves working closely with other teams such as finance, sales, marketing, and product development. The goal is to identify opportunities for process improvement, automation, and standardization, as well as to develop metrics and dashboards to track and measure business performance.
Several companies today store data from disparate units to merge static reports which are presented regularly to comply with regulatory requirements. Those companies who had already opted for the road to data modernization platforms have begun looking at merging data feeds into a single system, with online and live compliance testing.
Moreover, the data modernization end objective is to escalate the move to a fully dynamic aspect in which data can be managed between financial companies and regulators in real-time, through Agile Development and Operations (APIs) which can sort data based on regulatory needs at varying points in time, for varying products and services.
What are the Main Challenges for Banks to Overcome?
Data modernization for banks has three main challenges, that they should consider while driving to modernize their system and effectively react to their business needs. This includes:
- Reduce risk: Legacy systems used in organizations simply mean high exposure to risk significantly because of deep and unique levels of customization and the reduced standards of knowledge within companies as to how such complex technology functions. This is specifically the case in the areas of operational security and risk.
- Increase agility: Banking organizations need to cope with changing market needs and digital demand from the people and their associated businesses. Here, associating with third-party networks to drive innovation is significantly efficient. Banks are required to gain more hand in DevOps skills and to take advantage of the opportunities offered by APIs management.
- Generate insights: Banks are looking for solutions to manage the ever-increasing amount of data available in several formats. An efficient data management strategy is crucial to be able to compel valuable insights at a scale. Banks must take benefits offered by regulations like General Data Protection Regulation (GDPR) as a chance to fund modern systems which allow deeper customer understanding.
Effective Strategies to Guide Modernization
Banking organizations cannot imply new modern technologies easily; they need to put modernization efforts into their structure. The four common approaches discussed below are taken by most banks to reach their intended destination. Moreover, it is believed that the right strategy depends upon the time, risk, and cost associated with the bank.
- Customer engagement and servicing: Banks today focus on the techniques users use to interact with their bank. Banks are implementing modernization approaches that emphasize customer engagement, distribution layer, and servicing properties of the technological architecture.
Efforts include building reusable core activities which are available on several channels and that speak to users via their convenient and straightforward interfaces. This modernization service is the fastest and cheapest to market. - Customer experience and product enhancements: This strategy includes the distribution layer modernizing aspects along with some aspects of the transition and product processing layer. It provides an opportunity to integrate new products with enhancements to the overall customer experience. A major approach would be to define high-priority customer events like “resolve a fraudulent credit card charge” or “take out a mortgage” and at a time modernize an episode.
Customer and experience features could be defined from core product and transaction units like pricing or process orchestration, and risk assessment. Then, the bank defines a set of reusable and modern services for high-priority episodes. - Core focused: Changing the core sometimes roots from a bank fall off to provide changes to core products, customer experience, and regulatory needs. Those banks who have taken this approach commonly find their point of departure was a spaghetti tangle of units not able to manage real-time processes, with no distinction into how functions could be reformed.
Its front-end abilities often meet the current need for serving users but will fall if not able to handle core system problems. It is tough to replace main functions using a group of modern technologies and inherit core reforms into the other layers. Bank that opts for this strategy needs a high degree of discipline, fortitude, and engineering know-how. - Front to back: The “all-in” transformation process involves a comprehensive overhaul of all layers of a system’s architecture simultaneously. Although banks rarely implement this strategy for a fully operational Tier 1 entity due to the potential risks and disruptions it poses, it is commonly applied to subsidiaries. For instance, some banks create and test a model bank in one country that can be replicated in another, as seen in the case of DBS’s Digibank.
Other banks may first test a new modern technology stack in a greenfield setting before migrating their core customer base onto the latest technology. If successful, this modernization approach can give the entity a significant advantage over others. Key features of this approach include agile delivery of customer experiences through modern engagement and servicing systems, integration through APIs, and advanced data and analytics capabilities.
Overall, selecting the right strategy or approach depends upon a bank’s starting point, the main boost for transformation- from unsustainable costs to regulatory and operational risks—and the desired future state.
Artificial Intelligence in Banking
Artificial Intelligence (AI) is becoming increasingly prevalent in the banking industry, as it offers numerous benefits such as improved customer experiences, increased efficiency, and better risk management. Some of the most significant applications of AI in banking include:
- Chatbots and virtual assistants: These AI-powered tools can handle customer inquiries and requests 24/7, providing personalized support and reducing the workload on customer service teams. Additionally, AI can help banks to better understand their customers’ needs and preferences, enabling them to offer personalized products and services that meet individual needs.
- Fraud detection and prevention: AI algorithms can quickly identify suspicious activities and transactions, alerting the bank’s security team in real time. This not only helps prevent financial losses but also helps protect customers’ sensitive information.
- Improved risk management: By analyzing large volumes of data, AI algorithms can identify potential risks and predict future trends, allowing banks to make better-informed decisions regarding lending, investments, and compliance.
Data Modernization Will Drive the Future of Banking
Using a modern data environment, it is not possible to share data with regulators for risk management and compliance objectives. Banking and finance services companies can use modern data techniques to transfer or share data with their co-workers leading to increased efficiency and collaboration.
As banks are reforming their operational systems, which immensely depend upon wider ecosystems beyond their company (This means in the form of formal joint ventures or developing new products and services which collaborate with organizational abilities), the platform is required to adopt data modernization abilities for optimal linkage, governance, open banking, and innovation.
Regulation cannot be avoided in the banking and finance service sector. However, keeping ahead of the market reforms using data modernization can support financial companies to drive values that go far beyond compliance.
Moreover, AI is transforming the banking industry by enabling banks to provide more personalized and efficient services to their customers while improving risk management and fraud prevention. As technology advances, we can expect to see even more innovative applications of AI in banking.
