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Digital Transformation: A Historical Perspective
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.
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:
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.
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.
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.
“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.
“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.
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:
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.
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 (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:
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.