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Unlocking the Potential: Benefits of Enterprise Data Warehouse for Financial Services

In the ever-evolving landscape of financial services, data reigns supreme. Harnessing the power of data is not just an option; it’s a strategic imperative for banks looking to thrive in the digital era. Enter the Enterprise Data Warehouse (EDW), a robust solution that has emerged as a cornerstone for financial institutions seeking to navigate the complex terrain of data management and analytics.

Embracing the Data Revolution – A historical perspective

Recent statistics paint a compelling picture of the data landscape in financial services. According to a report by Statista, the global volume of data in the banking sector is expected to reach a staggering 122 exabytes by 2025, driven by the exponential growth of digital transactions, mobile banking, and emerging technologies. In this data-rich environment, the role of an Enterprise Data Warehouse becomes pivotal in extracting meaningful insights from the vast ocean of information.

1960s – The Emergence of Mainframes

In the 1960s, the BFSI sector witnessed the advent of mainframe computers, marking the beginning of electronic data processing. This era saw a shift from manual record-keeping to automated systems, laying the foundation for data-driven decision-making.

1980s – Rise of Database Management Systems

With the rise of Database Management Systems (DBMS) in the 1980s, banks started organizing and managing vast volumes of data more efficiently. This era saw the emergence of relational databases, providing a structured framework for storing and retrieving financial information.

1990s – Internet Banking and Customer Data

The 1990s ushered in internet banking, enabling customers to access financial services remotely. This shift increased the volume of data generated and highlighted the importance of securing customer information in the digital realm.

The early 2000s – Analytics and Business Intelligence

In the early 2000s, BFSI institutions began leveraging analytics and BI tools to extract insights from their growing datasets. This marked a strategic shift towards using data not just for operational purposes but also for strategic decision-making.

2010s – Big Data and Advanced Analytics

The proliferation of Big Data technologies in the 2010s allowed BFSI players to harness the power of massive datasets. AA, including ML and predictive modeling, became instrumental in risk management, fraud detection, and personalized customer experiences.

Present – AI, Machine Learning, and Digital Transformation

In the present era, Artificial Intelligence (AI) and Machine Learning (ML) are reshaping the BFSI landscape. Predictive analytics, chatbots, and robo-advisors exemplify how data-driven technologies enhance customer interactions and operational efficiency.

The impact of the data revolution is evident in the numbers. According to a McKinsey report, data analytics in banking can unlock $200 billion in value annually globally1. Moreover, a study by Statista predicts that global spending on AI in the banking industry will reach $12 billion by 2025.

The Fourfold Benefits

1. Unified Data Repository:

An EDW serves as a centralized repository, consolidating data from disparate sources within a financial institution. This unified view enhances data quality, reduces redundancy, and provides a holistic perspective crucial for informed decision-making.

2. Advanced Analytics and Reporting:

Leveraging the analytical capabilities of an EDW, financial institutions can derive actionable insights. Whether predicting customer behavior, managing risks, or complying with regulatory requirements, the EDW catalyzes advanced analytics.

3. Operational Efficiency:

Streamlining operations is a perennial goal for financial services. An EDW optimizes data management processes, offering a more efficient way to ingest, store, and retrieve data. This efficiency provides cost savings and improved agility.

4. Regulatory Compliance:

The financial sector operates in a highly regulated environment. An EDW facilitates compliance by providing a comprehensive and auditable data trail, ensuring that regulatory reporting requirements are met seamlessly.

Realizing the Vision: Noteworthy Examples

Several banks have embraced the EDW paradigm to innovate and edge ahead of their competition. JPMorgan Chase, for instance, invested significantly in an Enterprise Data Warehouse to enhance its data analytics capabilities, enabling better risk management and customer insights. Similarly, HSBC leveraged EDW to create a unified data platform, streamlining its operations and bolstering its ability to comply with regulatory standards.

Navigating the Challenges

Despite the myriad benefits, challenges abound in implementing Enterprise Data Warehouses. These include data security concerns, the need for skilled personnel, and the complexity of integrating legacy systems with modern data architectures.

Approaches to Success

To address these challenges, financial institutions must adopt a comprehensive approach. This involves investing in robust cybersecurity measures, upskilling their workforce, and gradually transitioning from legacy systems to more agile and scalable technologies. Collaboration with industry partners and leveraging cloud-based solutions are critical strategies for success.

Conclusion: The Path Forward

As financial services continue to traverse the data-driven landscape, the role of Enterprise Data Warehouses becomes increasingly indispensable. The benefits, from unified data repositories to advanced analytics, reshape how banks operate and strategize. To overcome the challenges ahead, a commitment to innovation, investment in talent, and a proactive stance towards evolving technologies will be crucial for financial institutions aiming to unlock the full potential of Enterprise Data Warehousing.

About Maveric Systems

Starting in 2000, Maveric Systems is a niche, domain-led Banking Tech specialist partnering with global banks to solve business challenges through emerging technology. 3000+ tech experts use proven frameworks to empower our customers to navigate a rapidly changing environment, enabling sharper definitions of their goals and measures to achieve them.

Across retail, corporate, and wealth management, Maveric accelerates digital transformation through native banking domain expertise, a customer-intimacy-led delivery model, and a vibrant leadership supported by a culture of ownership.

With centers of excellence for Data, Digital, Core Banking, and Quality Engineering, Maveric teams work in 15 countries with regional delivery capabilities in Bangalore, Chennai, Dubai, London, Poland, Riyadh, and Singapore.

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Banking’s Data Democratization: Insights and Lessons for Future`

The banking industry has changed profoundly in recent years, with data playing a pivotal role. Data in banking is no longer confined to silos but has become a democratized resource that drives innovation and change. This blog explores the concept of Banking’s Data Democratization, its impact on the industry, and the lessons it offers for the future. We will delve into banking transformation services, data in digital transformation, and big data in banking. Additionally, we will highlight recent examples of financial institutions leading the way in this data revolution.

The Rise of Data in Banking

Historically, banks were data-rich but often struggled to extract the needed help from their data. With the advent of technology and increased connectivity, the banking data concept has shifted dramatically. Here are the crucial aspects of this transformation:

Data Democratization

Data democratization is about making data accessible to all stakeholders within a bank. It empowers employees to access and utilize data for informed decision-making. This shift has been fundamental in modern banking.

Banking Transformation Services

Banks have embraced banking transformation services to democratize data, including data analytics, machine learning, cloud computing, and artificial intelligence. These services help banks efficiently manage and analyze data to extract valuable insights.

Data in Digital Transformation

Digital transformation in banking has been closely intertwined with data digital transformation. Banks leverage data to streamline processes, enhance customer experiences, and remain competitive digitally.

Big Data in Banking

The sheer volume of data generated in banking is staggering, and big data in banking has become a driving force. Banks use big data analytics to understand customer behavior better, detect fraud, and develop personalized services.

Remarkable Advances in Data Democratization

Several financial institutions have made significant strides in banking’s data democratization. Let’s explore some examples:

1)JPMorgan Chase:

JPMorgan Chase has established the “Data Analytics and AI Solutions” division, which harnesses the power of big data and AI to enhance decision-making. They have created advanced algorithms for credit risk assessment and portfolio optimization, improving the bank’s operational efficiency and customer service.

2) HSBC:

HSBC has implemented a data democratization strategy, ensuring that data is accessible to employees across the organization. They’ve also invested in data analytics tools to enhance their anti-money laundering efforts, protecting the bank and its customers.

3) Wells Fargo:

Wells Fargo uses data-driven insights to personalize customer experiences. They analyze transaction data to offer tailored product recommendations, improving customer satisfaction and loyalty.

Challenges and Approaches to Success

While banking’s data democratization holds enormous potential, it also presents challenges:

1) ata Security:

With increased data accessibility comes the need for robust data security. Banks must invest in state-of-the-art cybersecurity measures to protect sensitive customer information.

2) Regulatory Compliance:

Data privacy regulations are stringent. Banks must navigate complex compliance requirements while utilizing customer data.

3) Data Quality:

Ensuring data accuracy and quality is crucial. Banks need to invest in data governance and management processes.

4) Cultural Shift:

Democratizing data requires a cultural shift within banks. Employees must embrace data-driven decision-making.

Conclusion.

Banking’s data democratization is a transformative force in the industry. Notable institutions like JPMorgan Chase, HSBC, and Wells Fargo have harnessed the power of data to enhance their operations and customer experiences. However, challenges like data security and regulatory compliance persist. Success lies in investing in cybersecurity, adhering to regulations, ensuring data quality, and fostering a data-driven culture. The lessons learned from these innovations will shape the future of banking, where data is king and its democratization is the key to competitive advantage.

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Data Analytics Importance in the Finance and Banking Industry

Three things are coming together to start the next spotlight – Data Analytics in BFSI. First, think about how technology has changed. Information is becoming more and more accessible. In the past few years, the amount of valuable data—actual signal, not just noise—has grown exponentially while processors have gotten smaller and cheaper. Businesses have opened their minds and are willing to use new ways of analyzing data that might have been thought too academic and unrealistic for the real world. The exponential growth of computing power, which makes it possible to do in seconds an analysis that used to take weeks, and new tools for storing data, such as Hadoop, have made it much easier for any company to do these kinds of analytics.

Second, the economy is putting much pressure on banks in many places. According to McKinsey’s latest study, 54 percent of the top 500 institutions in the world are priced below their book value. In 2014, as the report posits, only 18% of banks got the industry’s value. Realizing this, banks have tried various ways to improve things, including going digital and cutting costs. But these moves can only get them so far. They need something new.

The third thing that is pushing analytics is these attempts to digitize. A standard bank has digitalized much of its work and puts data by terabytes. Advanced analytics would be hard to use in a bank that does most of its work by hand, but digital banks already have the roads built.

When you put everything together, you get advanced analytics, which are large-scale ways to use data to get real business insights and make much better decisions. The tools are there; now, banks need to use them to take steps that can lead to real change.

Partnering with domain experts in Data Technology and Big Data Analytics like Maveric Systems brings a decisive edge for FIs to embrace next-generation technologies and create industry benchmarks.

Importance of Data Analytics in the Banking and the Financial Sector

Advanced analytics can help banks make small changes to almost everything they do daily, boosting the standard P&L levers. Here are some things that could be done:
Together with transactional and trading analytics, deeper and more detailed profiles of customers can help get and keep customers, as well as cross-sell and up-sell. For example, one bank used information about credit card transactions (from its terminals and those of other banks) to make deals that encouraged customers to buy from one of the bank’s merchants frequently.
Increasing work output. Every step of the banking process can be made faster and better. Banks can use advanced analytics to respond to governmental requests quicker and more accurately and help teams make decisions with the help of analytics.
Improving risk control. Compliance and control costs have increased significantly in the past few years, and banks can use analytics to get a return on their significant investments. With the help of analytics, banks can use tools like digital credit assessment, advanced early-warning systems, next-generation stress testing, and credit-collection analytics to lower their risk costs.
Customer Experience: Another way analytics can have an effect is by helping digital banks live up to their promises and give customers a much better experience for a fraction of what it costs now. In some places, up to 65% of people now talk to their banks in multiple ways. Their paths through them are very complicated. They often start in one channel, do steps in others, and end in another, with many stops and turns to gather information. Successful digital banks offer a smooth multichannel experience by collecting real-time data and using analytics to understand the customer and build the right (and always consistent) journey view.
Lastly, analytics can help banks find new ways to grow their businesses and even new ways to do business. Banks can profit from their data by letting new ecosystem partners, like telecom companies or stores, use their customer analytics tools. As a data company, the bank can be at the center of a customer environment where banking and many other B2C and B2B businesses bring in money. Taken to an extreme that is reasonable but not impossible, banks can use data and analytics to create a new business model and beat fintech companies at their own game.

Conclusion

Data analytics has become essential to how small and big businesses make decisions. The vast amounts of structured and unstructured data made by many gadgets on different platforms have given us unique insights. With the help of data analytics and data management, the Banking and Financial Services Industry (BFSI) has used Big Data to improve organizational success and ensure risk management, financial growth, and performance. The main goals of the banking and economic areas are to improve performance, make money, and cut down on risk. In this data-driven world, success depends on Big Data technologies that can store and handle semi-structured and unstructured data in real-time.

About Maveric Systems

Starting in 2000, Maveric Systems is a niche, domain-led Banking Tech specialist partnering with global banks to solve business challenges through emerging technology. 3000+ tech experts use proven frameworks to empower our customers to navigate a rapidly changing environment, enabling sharper definitions of their goals and measures to achieve them.

Across retail, corporate & wealth management, Maveric Systems accelerates digital transformation through native banking domain expertise, a customer-intimacy-led delivery model, and a vibrant leadership supported by a culture of ownership.

With centers of excellence for Data, Digital, Core Banking, and Quality Engineering, Maveric Systems teams work in 15 countries with regional delivery capabilities in Bangalore, Chennai, Dubai, London, Poland, Riyadh, and Singapore.

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How Are Data Analytics Used In The Banking And Finance Industries?

Financial institutions use data and predictive analytics to improve customer experience and amplify business success. Today, banks want more than incremental gains. They want data-driven revenue breakthroughs. Banks increasingly rely on data. It’s the future of communication and might address banks’ most significant issues.

Data drives today’s financial market. The information helps financial services businesses personalize, streamline, and profit. This change affects operating costs, staffing, customer experience, and more.

Engaging banking domain experts like Maveric Systems for next-generation data technology solutions enable leading banks to make bold bets and maximize their innovation potential.

The Growing Use Cases for Big Data Analytics for BFSI

Fraud Detection: Banks and financial organizations aim to reduce fraud, but analytics may also control risk. Banks can prioritize fraud detection by assessing account risk. Analytics can detect and rate fraud-prone consumers and apply different levels of monitoring and verification to those accounts.
Operational/liquidity risk: Operational risk is the risk of company losses. Operational risk includes financial institution-specific hazards. Liquidity risk includes macro, comprising interest rate swings, foreign exchange rate changes, and bond value changes. Operational hazards include fraud, theft, computer security breaches, and management incompetence. Banks utilize data analysis to identify high-risk loan defaulters and act before things get out of hand.
Investment bank risk modeling: Risk modeling simulates how a portfolio of assets (stocks, bonds, futures, options, etc.) or a single asset (such as an interest rate) reacts to different scenarios. Risk modeling across all assets reduces portfolio risk and improves performance.

Data-Driven Banking

Technology and analytics enhance strategic planning and decision-making. Data-driven banking rethinks banking data. Instead of displaying account information, currency fluctuations, or analyzing your data, banks use it to add product and service information to their products. This gives banks more insight into their customers’ lives and helps them increase earnings by delivering more accurate data about their clients’ demands.

This helps customers make more competent judgments and allows third-party companies to promote a bank’s products, which benefits society.

Three Key Components for creating a holistic Data Analytics Strategy

Data Volume: We realize “Data Analytics” demands “excellent data,” but volume comes first. Many corporations tend to ignore volume. They think it’s available. Building a data bank is an enterprise problem today; only a few businesses take it seriously. Data collection, curation, and storage systems require the correct personnel and time at project start-up. Not establishing a data warehouse and putting everything there is the data team’s primary business goal.
Data Accuracy: Data quality follows volume. More data is needed to ensure relevant insights/models. It reduces data team productivity. Data workers spend much of their time reassembling data to improve quality. Data scientists spend over 82% of their time cleaning and preparing AI/ML data. Organizations need good data pipelines and reliable data flow infrastructure for accurate insights.
Data Security and Compliance: Organizations should prioritize data governance, security, and compliance when establishing a data strategy rather than technology-focused data volume and quality. A data analytics roadmap needs the correct mix of people, data, products, technology, process, and governance to be productive. An analytics roadmap should also begin with thoroughly analyzing the company’s business goals and key indicators.

Conclusion

Finance is increasingly using data analytics. Data analytics is helping more companies enhance internal processes worldwide. Data analytics helps them understand customers better. This allows CEOs to make smarter business decisions. Finance professionals use data analytics to gain insights that improve decision-making. Finance teams use data analytics to understand KPIs (KPIs). Revenue, net income, payroll, etc. Data analytics helps finance teams analyze fundamental KPIs and spot revenue turnover fraud.

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How DevOps will help FIs to advance the use of Automation

Ninety-nine percent of respondents to a recent Atlassian Survey – DevOps Trends state that DevOps has positively influenced their firm. There is a reason why post-COVID, DevOps has an increased preference for leading banks and FIs.

Amidst the epidemic, some financial institutions were compelled to augment their digital service to accommodate consumers’ evolving desires. Transformations of the front end, such as digital account opening, Know Your Customer, mobile banking, online customer assistance, and self-service, must be completed quickly.

This significant effort to improve the digital customer experience (CX) has had a ripple effect on backend systems and supporting IT operations. A rising digital footprint spanning on-premises software and the cloud necessitates improved cadence for managing new deployments, target infrastructure, and legacy cores with new digital add-ons. Working with deep domain experts – like Maveric – creates market-defining value propositions.

How is DevOps offering game-changing rhythms in the BFSI sector?

Robotic Process Automation

DevOps significantly accelerates internal procedures within the financial services business. Automating tasks across the process chain or software delivery cycle effectively achieves efficient resource utilization, improved product quality, and increased developer productivity while balancing responsibilities across cross-project requirements, frequent updates, and enterprise-level process management.

Security

Security is the fundamental reason financial service organizations embrace DevOps as part of their digital transformation efforts. Even though the rapid delivery method of DevOps was previously perceived as a security risk, numerous financial institutions have claimed improved security and faster recovery due to DevOps principles.

Creating a performance culture

DevOps is a combination of Development (Dev) and Operations (Ops) that is valued more for the organizational culture it fosters than for the technology itself (Ops). DevOps culture facilitates the concurrent functioning of development, operations, and Quality Assurance (QA), promoting effective collaboration and knowledge-sharing, thereby easing internal processes. DevOps approaches unite several teams on a single platform while connecting them to a project pipeline.

Effective Management Outcomes

Large firms frequently need help with their widely dispersed internal teams, which may result in misunderstandings that disrupt the process flow when necessary. DevOps handles all steps of the software delivery pipeline (release, deploy, test, and build) to ensure shared visibility and process control between teams. This includes preventing differences between groups and eliminating functional silos by properly addressing every component of the software delivery chain.

Creating Tomorrow’s Modernization Dynamics

Digital banking is nothing new. DevOps enables you to modernize systems continuously without putting your business at risk by bringing it offline. Alternately, if companies start from scratch, DevOps enables modernization at the construct stage, responding to the current trends without the outcome that the newly-released solution is already obsolete.

Boosting Transparency

Previously, financial software was developed independently using a very hierarchical procedure. Participants worked effectively in silos, focusing on their part of the assignment and nothing else, which was beneficial for information security but hampered efficiency. All of this has changed. DevOps in finance necessitates the participation and collaboration of multiple teams, including QA, engineering, content, compliance, legal, etc., to guarantee that the software solution is up to par. The an

Conclusion

The rapid delivery of services has become crucial for businesses across all industry verticals to compete in the current market environment. Financial services are essential businesses characterized by high customer reliance and the consequent necessity for rapid delivery of solutions to fulfill the ever-increasing end-user requirements. As a sector that engages in high-level financial operations, such as banking, financial services necessitate uninterruptible client service and a solid internal infrastructure for streamlined processes.

About Maveric Systems

Across retail, corporate & wealth management, Maveric accelerates digital transformation through native banking domain expertise, a customer-intimacy-led delivery model, and a vibrant leadership supported by a culture of ownership.

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Embracing the Data Revolution – A historical perspective

1960s – The Emergence of Mainframes

1980s – Rise of Database Management Systems

1990s – Internet Banking and Customer Data

The early 2000s – Analytics and Business Intelligence

2010s – Big Data and Advanced Analytics

Present – AI, Machine Learning, and Digital Transformation

The Fourfold Benefits

1. Unified Data Repository:

2. Advanced Analytics and Reporting:

3. Operational Efficiency:

4. Regulatory Compliance:

Realizing the Vision: Noteworthy Examples

Navigating the Challenges

Approaches to Success

Conclusion: The Path Forward

About Maveric Systems

The Rise of Data in Banking

Data Democratization

Banking Transformation Services

Data in Digital Transformation

Big Data in Banking

Remarkable Advances in Data Democratization

1)JPMorgan Chase:

2) HSBC:

3) Wells Fargo:

Challenges and Approaches to Success

1) ata Security:

2) Regulatory Compliance:

3) Data Quality:

4) Cultural Shift:

Conclusion.

About Maveric Systems

Importance of Data Analytics in the Banking and the Financial Sector

Conclusion

About Maveric Systems

The Growing Use Cases for Big Data Analytics for BFSI

Data-Driven Banking

Three Key Components for creating a holistic Data Analytics Strategy

Conclusion

About Maveric Systems

How is DevOps offering game-changing rhythms in the BFSI sector?

Security

Creating a performance culture

Effective Management Outcomes

Creating Tomorrow’s Modernization Dynamics

Boosting Transparency

Conclusion

About Maveric Systems