Evolving into an AI-First Bank

AI has the potential to revolutionize banking by enhancing customer experiences, improving operational efficiency, and uncovering new opportunities. According to McKinsey, AI could add up to $1 trillion in value annually to global banking. Banks that successfully integrate AI into their operations are well-positioned to lead in this evolving landscape.

Let us look at a few key areas in a diagram where AI can improve the customer experience while simplifying backend processes.

Key Areas Where AI Can Make an Impact

Personalized Services

AI customizes product recommendations and services for each customer, enhancing satisfaction and fostering loyalty. Through extensive data analysis, it gains insights into customer behaviors, preferences, and needs. This enables banks to offer personalized financial advice, customized product offerings, and proactive customer service, resulting in enhanced customer experiences. For example, AI-driven chatbots provide instant customer support, addressing queries and offering personalized financial advice based on individual spending patterns and financial goals.

Operational Efficiency

Automation reduces errors and optimizes resource utilization. AI-driven automation streamlines back-office operations, reduces manual intervention, and minimizes human error. This leads to faster processing times, cost savings, and improved accuracy in tasks such as data entry, document verification, and transaction processing.

Example: Robotic Process Automation (RPA) can handle routine tasks such as account opening, loan processing, and compliance reporting, freeing up human employees to focus on more strategic activities.

Risk Management

AI enhances fraud detection, credit risk assessment, and compliance monitoring. By leveraging machine learning algorithms and predictive analytics, AI can identify unusual patterns and behaviors indicative of fraudulent activity. Additionally, AI can assess credit risk more accurately by analyzing a wider range of data points, including non-traditional data sources.

Example: AI-powered systems can monitor transactions in real-time, flagging suspicious activities and preventing potential fraud before it occurs. In credit risk assessment, AI models can evaluate the creditworthiness of borrowers more precisely, leading to better-informed lending decisions.

Importance of Data Governance in BFSI Institutions

The BFSI (Banking, Financial Services, and Insurance) sector manages extensive amounts of sensitive data, necessitating strong data governance frameworks. Robust data governance is vital for ensuring compliance with regulatory standards, protecting data security, and maintaining high data quality. These elements are crucial for upholding trust, ensuring operational integrity, and enhancing the overall efficiency of BFSI institutions.

Core Principles of Data Governance

1. Comprehensive Data Quality Management (DQM)

• Continuous Assessment: Regular monitoring and assessment of data quality to identify and rectify inaccuracies, inconsistencies, and incompleteness.
• Improvement Processes: Implementation of strategies and tools for continuous data quality improvement, ensuring data remains reliable and accurate over time.
• Data Standards: Establishing and enforcing data standards and guidelines to maintain consistency and reliability across all data sources.

2. Stringent Security Measures

• Data Protection: Implementing advanced security measures such as encryption, access controls, and multi-factor authentication to safeguard data against breaches and unauthorized access.
• Incident Response: Developing and maintaining an effective incident response plan to quickly address and mitigate any security breaches or vulnerabilities.
• Risk Management: Continuously assessing and managing risks associated with data security to preemptively address potential threats.

3. Regulatory Compliance

• Adherence to Regulations: Ensuring compliance with regulations like GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act), which govern data privacy and protection.
• Audit Trails: Maintaining detailed audit trails and documentation to demonstrate compliance with regulatory requirements and to facilitate regulatory audits.
• Regular Audits: Conducting regular internal and external audits to ensure ongoing compliance with relevant regulations and standards.

4. Efficient Data Access

• Role-Based Access: Implementing role-based access controls to ensure that data is accessible only to authorized users based on their roles and responsibilities.
• Data Availability: Ensuring data is readily available to authorized users when needed, supporting timely decision-making and operational efficiency.
• User-Friendly Interfaces: Developing intuitive and user-friendly data access interfaces to facilitate easy and efficient data retrieval.

5. Data Lifecycle Management

• Data Creation and Acquisition: Implementing standardized processes for data creation and acquisition to ensure data integrity from the outset.
• Data Maintenance: Regularly updating and maintaining data to ensure its accuracy and relevance.
• Data Disposal: Establishing secure data disposal practices to safely and effectively remove data that is no longer needed, ensuring compliance with data retention policies and regulations.

6. User Education and Accountability

• Training Programs: Conducting regular training programs to educate users about data governance policies, procedures, and their responsibilities.
• Policy Awareness: Ensuring all users are aware of and understand the data governance policies and the importance of adhering to them.
• Accountability Mechanisms: Implementing mechanisms to hold users accountable for their data-related actions, encouraging responsible data handling and usage.

Conclusion

By embracing AI and implementing robust data governance frameworks, BFSI institutions can unlock new growth opportunities and enhance customer experiences. AI-driven analytics can provide deeper insights, improve decision-making, and drive innovation. Effective data governance ensures these AI initiatives are built on a foundation of high-quality, secure, and compliant data.

A successful legacy modernization involves developing a scalable, reliable, and maintainable enterprise data strategy. This strategy not only supports current operational needs but also prepares the institution for future growth and regulatory changes. By staying relevant and increasing growth through these means, BFSI institutions can maintain their competitive edge in an evolving financial landscape.

Ultimately, robust data governance combined with advanced AI capabilities positions BFSI institutions to thrive in the digital age, ensuring they remain compliant, secure, and customer-focused.

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Key Elements in Data Modernization Roadmap

The data modernization process focuses on developing a modern data framework that includes a series of steps and techniques, with modern technologies at its core. A strong data modernization strategy includes a few critical elements that enable financial enterprises to increase ROI by creating an accessible, scalable, and compliant data ecosystem. The diagram below exhibits them all in one place. The institute can either consider these milestones and achieve them individually, or alternatively, logically group the elements and accomplish them together.

1. Data Migration

The goal of data migration is to move all your data from legacy platforms to a modern data infrastructure. A solid data migration strategy ensures a smooth transition of clean data from an old to a new and modern data platform with no or little business disruption. And here’s where the cloud migration comes in.

The size of the cloud migration market is estimated at USD 232.51 billion in 2024 and is expected to grow at a CAGR of ~28% during the forecast period (2024–2029).

Globally, many finance institutes have already switched to cloud platforms or are accelerating their migration owing to benefits such as scalability, agility in integration, and adoption of emerging technologies in the BFSI ecosystems. In recent years, cloud adoption has been a significant consideration for IT cost-reduction strategies.

Data migration is a critical step in the data modernization process. There are obstacles, but they are worth crossing. Completing this step simplifies the remaining milestones.

2. Data Integration

Building a unified data view through data integration further enables the use of the data for meaningful consumption in analytics and AI frameworks. In a well-structured data modernization strategy, appropriate cloud-hosted ingestion tools can be in place, allowing data integration solutions to be scalable, robust, and easily accelerated. Modern extract-transform-load (ETL) tools empower real-time integrations, ensuring the quickest data availability. This is crucial in BFSI, where up-to-date information is critical; a prime example is the live and accurate integration of financial transactions. This allows institutes to offer more cutting-edge services, thus increasing customer satisfaction.

3. Data Cleansing & Transformation

Data transformation is essential for harnessing the full potential of data assets, enhancing efficiency, and driving business value. Transformation can involve various operations, including data cleansing. This cleaning process eliminates errors, inconsistencies, and missing values from a large volume of ingested data, resulting in high-quality, reliable data for analytics and AI/ML solutions. Data transformation can also include standardization, aggregation, encoding categorized data, binning, smoothing, time-series decomposition, text preprocessing, etc. The choice of transformation depends entirely on the organization’s use case, the nature of the data, or the specific goals that the analysis or modeling task aims to accomplish. Comprehensive data transformation allows everyone in the organization to better understand the data and makes it easier to work with.

4. Data Modelling, Storage & Management

Banks and financial services institutions collect a lot of data from customers’ online and offline transactions, social engagements and interactions, feedback, surveys, and more. Data modeling is a discipline widely applicable to any intersection of people, data, and technology. It is a well-defined approach to creating an illustrated data model to organize its attributes, establish relationships between objects, identify constraints, and define the context to manage the data. With data integration in place and connecting to various systems, it is imperative that a data model be established before moving to data consolidation and storage. Legacy data frameworks have followed this essential step, and their modern counterparts cannot eliminate it either.

As the organization grows, the volume of data also increases. A well-laid-out data storage management plan removes the snag of having big data stashed across multiple systems, with users creating multiple copies (which is not ideal), and empowers BFS organizations to store data efficiently and securely, in compliance with laws, making the data easy to find, access, share, process, and recover if lost. An organization can choose in-house infrastructure, cloud, or a hybrid platform for their data storage. A data framework may consist of one or more data layers, each serving distinct purposes and storing data uniquely. Depending on the organization’s vision, one may choose a relational database, a data lake, a lakehouse, a data mesh storage architecture, or a combination of these.

5. Data Rules, Object-Oriented DBMS, and Polymorphic Data Store

A large amount of invalid or erroneous data can disrupt end services and create unpleasant experiences for both customers and the organization. Therefore, a rule-based data validation approach, when systematically combined within the data integration framework, consistently produces superior quality data. It is often necessary to store a segment of data as an object, in addition to traditional structured data storage systems like databases. Object-oriented DBMSs combine the features of object-oriented and database management systems to store complex data. They enforce object-oriented features like encapsulation, polymorphism, and inheritance, along with database concepts like ACID properties. Banking organizations may consider having an ODBMS while developing high-volume transactional websites. It could also be useful for a risk management application because it provides a real-time view of the data.

6. Data Quality Management

Failing to ensure data quality can have a ripple effect across a bank. Low data quality impacts privacy compliance, leading to more mistakes and, more often, significant failures for a business operating within one of the world’s most highly regulated industries and dealing with a vast volume of sensitive data. Privacy compliance is only the beginning. Regional banks and credit unions must also adhere to various reporting compliance regulations, including laws covering fair lending, customer data protection, enabling customers to make informed decisions, accurate disclosure, anti-money laundering, and capital adequacy. Failure to do so can lead to severe fines and reputational damage.

Beyond compliance, inadequate data quality can lead to poor decision-making. This is particularly important when it comes to managing risk. Banks need the highest quality data at their disposal to manage risk effectively, which is one of the most critical functions the organization must undertake. A comprehensive data quality management (DQM) framework should be in place on all data platforms, especially in the BFSI sector. We broadly recommend five key steps to cover all the bases of DQM:

• Assessing the quality of organizational data is the starting point. Techniques such as data profiling can be used to inspect and understand the data’s content and structure.
• Create a data quality strategy to improve and maintain the data’s quality. It is a continuous process and can be based on an enterprise-level set of rules or specific use cases. The organization can decide whether to use a feature-rich DQ tool or a custom module.
• Perform initial data cleaning, an action to improve the data. It can be as simple as identifying missing entries, completing them, or removing duplicate entries.
• Implementation of the Data Quality Framework is where the strategies come into action. It should be a seamless integration, whether with the data integration process or the business process. DQM should eventually become a self-correcting, continuous process.
• Monitor the data quality processes, ensuring DQM is not just a one-time event. Constant monitoring and maintenance, as well as timely review and updates, are the only ways to maintain high data standards.

7. Data Warehousing
   A data warehouse centralizes and consolidates large amounts of data from disparate sources. Its analytical capabilities allow organizations to derive valuable business insights from their data to improve decision-making and provide better services and experiences. The banking sector can instantly reap numerous benefits from a well-designed data warehouse, including the following key advantages:
   
   
   
   To handle the massive amounts of data generated by banks, technologies like Hadoop and Spark have become the go-to choices for storing and processing unstructured and semi-structured data. They provide a scalable and cost-effective solution for data warehousing. More and more institutes are moving towards cloud data warehousing solutions like Snowflake, Azure Synapse Analytics, Amazon Redshift, or Google BigQuery to leverage all the benefits they offer in terms of scalability, efficiency, and accessibility.
8. Data Analytics & Data Democratization
   The organization does not immediately benefit from having a large, consolidated volume of data in a data warehouse until it uses that data to provide insights, improve services, and enhance experiences for both customers and organizational users. One popular use case where data analytics plays a critical role is in loan disbursement. In a traditional banking platform, loan disbursement often involves document submission, verification, and due diligence from the bank side, and each of these steps contributes to delays. However, to gain a competitive advantage, neo-banks are leveraging data analytics on real-time data, allowing them to make decisions almost instantly.In essence, data analytics is crucial for banks to enhance operations, explore potential opportunities, identify target demographics for upcoming campaigns, or simply upsell their products. Here are some other key use cases where data analytics will soon become indispensable for banks:
   • Providing 360-degree insights on a customer.
   • Understanding the operations and services through data, performing predictive analytics, and upgrading features to reduce operational costs.
   • Understanding target customer demographics, categorizing them based on data insights, and providing them with a personalized experience.
   • Examining risks associated with credit, claims, and fraud, and improving risk management practices.
   • Understanding market trends, embracing emerging practices, and staying ahead of the curve.

   Data democratization is an enablement process to make data available to everyone in an organization. If a marketing manager wants to access some of the reports created through data analytics, and if that involves IT, it delays decision-making. Democratization of data eliminates siloed or outdated practices. It encourages users to truly use the data, empowering them to identify new opportunities, create revenue streams, and drive growth. Data democratization can safely unlock access to data stored in a data warehouse, lake, or lakehouse. The modern data framework can avoid distributed data access, a challenge on legacy platforms, by connecting them through a single interface.

Conclusion

By following these key elements and best practices, financial institutions can build a modern data framework that not only supports their current needs but is also scalable and future-proof. This strategic approach ensures data is managed efficiently, securely, and used to drive meaningful business outcomes. Embracing data modernization helps BFSI organizations stay competitive, comply with regulatory requirements, and deliver superior customer experiences in an increasingly data-driven world.

Implementing a modern data framework is a journey that requires careful planning and execution. However, the benefits of such a transformation—enhanced agility, better decision-making, and improved operational efficiency—make it a worthwhile investment for any forward-thinking financial institution.

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On April 8, 2024, the European Union`s Instant Payments Regulation came into force, making instant Euro payments fully accessible to consumers, businesses and investors throughout the EU and EEA countries.

Need for instant payments and the key beneficiaries

The key aim of instant payments is to ease the cash flow for all players especially retail clients, SMEs, NGOs and other smaller institutions by ensuring immediate credit at a cost that is not more than any other traditional payment mechanism. As a reference, in 2022, only 11% of the payments were executed within seconds. Nearly €200bn is locked in transit on any given day. Instant payments will unlock this lost value and ease cash flows delays in which typically stymie the smaller businesses. This will also pave way for EU in its attempt to reduce the extensive dependence on foreign financial operators like Visa and Mastercard to strengthen its own financial autonomy.

Salient features:

• Instant credit transfers processed 24×365 and within 10 seconds with receipt generation
• Instant conversion to Euros by PSPs if payment is submitted from a non-Euro account
• PSPs should have established robust fraud detection measures to prevent transfers to a wrong person
• PSPs to have extra measures to prevent criminal activities such as money laundering and terrorist financing
• Instant payments should cost no more than the traditional transactions in Euros
• The Instant Payments Regulation amends the Settlement Finality Directive to allow payment and e-money institutions (PIEMIs) access to payment systems, mandating instant credit transfer services after a transitional period. It also implements safeguards to mitigate any additional risks to the system.

How does this translate into a key technology and operational impact for PSPs?

Implementing instant payments will call for a full impact assessment across the lifecycle of a transaction. Broadly it will cover the channels from which the transactions are generated, payment routing technology (the scheme/rail against which the payment needs to be routed and in this case, the Instant payment connecting to RT1 system), pre-transaction checks including new rule bases, customer screening, beneficiary validation impacting either the master data modules or the payments hub technology in play at an institution. In addition, the accounting impact to reflect the value date to equate with transaction date may call for changes to ERP as well as core banking engines which would record the journal entries for each transaction.

Some specific and critical impact areas for institutions to bear in mind are:

• Instant credit will mean value date to remain the same as the transaction date irrespective of which calendar day it This means introduction of rules in payments technology to override holiday calendars that traditionally have been applicable to payment settlements
• Instant euro conversion will mean the payment engines in play at PSPs are geared to convert non-euro payments into euros instantaneously and the rule bases are established accordingly
• Payee verification / confirmation of payee becomes a mandatory feature to be implemented to prevent payments getting routed to the wrong person. Under the new regulations, instant payment providers must confirm that the beneficiary’s IBAN (International Bank Account Number) and name correspond to one another, enabling them to notify the payer of potential errors or fraud before the transaction is completed
• Daily verification of the entire customer base of an institution against all relevant and applicable sanctions list becomes mandatory for each and every PSP. Furthermore, if any of the sanctions list as applicable to a PSP is updated intra-day by the relevant regulatory body, the sanctions screening will also need to be run intra-day by each and every institution responsible for processing a payment
• There would be rule bases in payments technology to be implemented to ensure that instant payments are not levied with charges that exceed the traditional credit transfers
• There is a cap of €100,000 per transaction. This calls of introduction of a new rule base with a logic to identify a transaction that is pre-agreed to be executed instantaneously if it exceed this value.
• Since the Instant Payments Regulation will enable PSP, including all organizations licensed to offer payment services within the European Union, such as banks, payment services providers, and mobile payment providers, some of these players will need to embark on implementing the entire technology framework to execute instant payments.

Implementation timelines as applicable to institutions. Failure to implement this would mean that institutions can be fined upto 10% of their turnover:

About Experion

Experion Technologies is a Global Product Engineering Services company offering enterprises future-ready and transformative digital solutions. The company is a partner to 500+ global customers across 36 countries, driving new revenue streams and digitalizing businesses for Fortune 100 and Fortune 500 companies in the Healthcare, Retail, Transport and Logistics, BFSI, Construction and Engineering, and EdTech sectors.

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Experion’s expertise in data modernization empowers banking institutions to seamlessly integrate and optimize their data infrastructure, enabling real-time analytics and enhanced decision-making. In an era where data drives decision-making and customer experiences, the Banking, Financial Services, and Insurance (BFSI) sector is increasingly recognizing the need for data modernization. Despite the clear benefits, many financial institutions struggle with legacy systems and data quality issues. This blog explores the challenges and the critical steps needed for successful data modernization.

The Need for Data Modernization

The past two decades have transformed banking operations significantly. The shift towards virtual banking and digital payments accelerated due to the financial crisis of 2008 and the recent pandemic. Today, data plays a crucial role in defining enterprise goals and achieving business growth. The key to success lies in leveraging available data and strategically architecting it to attract new customers, maintain existing ones, and enhance customer satisfaction. Transitioning from legacy infrastructures to a modern data architecture that supports data-driven operations and analytics with high security and governance is imperative.

Challenges in Data Modernization

Customer Experience and Expectations

Modern customers expect seamless, personalized experiences, particularly in mobile and online banking. However, many institutions struggle to prioritize the customer experience. Outdated systems are often incapable of delivering the personalized and efficient services that customers now demand. This gap can lead to dissatisfaction and churn.

Lack of Customer Intelligence

The absence of a “Single View of Customer” hampers effective customer segmentation and engagement. Without a unified view, banks cannot fully understand their customers’ needs and behaviors. Implementing a comprehensive customer intelligence system can significantly improve customer interactions and loyalty by providing actionable insights into customer preferences and trends.

Ineffective Digital Transformation

Digital transformation efforts fall short without a robust data platform. Banks need to harness the power of their data to innovate and stay competitive. However, many are held back by fragmented data systems that limit their ability to make data-driven decisions and offer advanced services.

Data Abundance

Managing the volume, velocity, variety, and veracity of data is a significant challenge. Ensuring data accuracy, comprehensiveness, and coherence is crucial for meaningful insights. With the exponential growth of data, traditional systems often struggle to keep up, leading to data silos and quality issues.

Competition from FinTech

Neo-banks and FinTech companies are setting high standards in customer service and efficiency. These agile competitors leverage modern technologies to offer superior services and products. Traditional banks must modernize rapidly to stay competitive and meet evolving customer expectations.

Here is a simplified view of the core challenges that the banking institute might face if they are still operating with a legacy data platform. Addressing them through a modern data platform should always be the key objective.

The First Step Towards Data Modernization

Experion’s expertise guides organizations in selecting the optimal solution that balances cost, scalability, and performance to meet their unique business needs. The initial decision is whether to build the modern data platform on-premises or in the cloud. Both approaches have their merits and challenges.

On-Premises

Many businesses continue to use traditional methods with in-house infrastructure. On-premises solutions offer control over data and systems, which can be critical for regulatory compliance and security. However, they come with significant challenges, such as high upfront costs, limited scalability, and the need for ongoing maintenance and upgrades.

Cloud Platforms

Cloud platforms offer scalability, flexibility, and the ability to handle multiple inconsistencies at once. They enable rapid deployment, reduce infrastructure costs, and provide access to advanced analytics and AI tools. Cloud solutions can also enhance collaboration and data sharing across the organization. However, they require careful management of data security and regulatory compliance.

Many businesses continue to use traditional methods (with in-house infrastructure), albeit with some complications and challenges. Perhaps the effectiveness diminishes over time as the business landscape undergoes changes due to technological disruption. The modern approach (the cloud data platform) has a distinct pace and benefits that can handle multiple inconsistencies at once. This diagram highlights the key differences between traditional and modern data platforms:

Conclusion

Overcoming these challenges with a strategic approach to data modernization can enable banks and financial institutions to enhance customer satisfaction, improve operational efficiency, and stay competitive. The journey towards data modernization begins with the right decisions about infrastructure and a commitment to leveraging modern technologies. By embracing cloud solutions and advanced data analytics, financial institutions can unlock new opportunities for growth and innovation.

Modernizing data platforms is not just a technological upgrade but a strategic transformation that positions BFSI institutions for future success. It requires a clear vision, strong leadership, and a willingness to invest in new technologies and processes. Ultimately, the benefits of enhanced customer experiences, operational efficiencies, and competitive advantages make data modernization a crucial priority for the BFSI sector.

With Experion’s tailored data modernization services, banks can drive transformative growth, enhancing their competitive edge and delivering superior customer experiences through streamlined, data-driven processes.

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