Make data-driven decisions like the pros. Learn how major global banks use data analytics.<\/p>\n
What is data-driven banking?<\/h2>\n
Data-driven commercial banking applies advanced analytics, AI and other techniques to financial data to get actionable insights. AI- and ML-driven insights are used to make corporate banking operations more efficient and improve customer experience. Unlike legacy systems that provide static reports based on historical data, modern data-driven FinTech systems can process information in real time from various data sources. The benefits are significant. McKinsey<\/a> says the banking sector could achieve $200\u2013$340 billion in value by taking advantage of all generative AI capabilities.<\/p>\n Many innovative banking solutions use AI and ML to identify patterns in financial data. Among other benefits, they help banks provide more personalized financial services. For example, previous transactions could help a bank determine when a customer might be ready to make a major purchase. Using predictive analytics, the bank could offer a loan that provides interest rates and payments based on that customer\u2019s financial history or projected future spending.<\/p>\n There are many benefits to becoming data-driven. Data and analytics<\/a> allow banks to:<\/p>\n The promises of becoming a data-driven organization are too strong to ignore. Financial institutions that have already started their data-driven transformation find it improves operational efficiency and mitigates risks. Once companies begin to see the importance of investing in new technologies, they are driven to find even more opportunities for improvement.<\/p>\n Benefits banks expect from data and analytics solutions<\/strong><\/p>\n Source: Tableau and FinTech Futures, The Power of Data Analytics in FinTech Solutions<\/a><\/em><\/p>\n Here are several benefits banks expect from AI, data and analytics:<\/p>\n While the potential benefits of data-driven banking are substantial, some banks encounter challenges that slow the adoption of new technology and reduce its effectiveness. One is the availability of high-quality data. Historically, many banking systems siloed their data because they were managed by separate departments. A central data management<\/a> system eliminates those silos and makes data available to the entire organization for daily decision-making.<\/p>\n Source: McKinsey & Co.: The data and analytics edge in corporate and commercial banking<\/a><\/em><\/p>\n Another challenge is legacy banking systems. Many financial institutions still rely on technology that was designed decades ago. Legacy platforms notoriously fragment data. As a result, data is not available for daily decision-making. For example, a legacy platform might store transaction histories in a different location from loan records, making it difficult to obtain a unified view of customer behavior.<\/p>\n Data quality is yet another concern. AI and ML algorithms need clean, reliable and consistent data to produce meaningful insights. Establishing a data governance framework provides data and AI oversight that maintains accuracy and consistency across the organization.<\/p>\n Where do banks stand on data?<\/strong><\/p>\n Some banks also face internal resistance. Employees are accustomed to legacy banking systems and may not support changes. In many cases, a lack of understanding or unmitigated fear is the reason behind their resistance. The rate of adoption improves when software users are properly trained.<\/p>\n Finally, cost constraints further complicate the journey to becoming a data-driven bank. Some financial institutions have delayed system replacements for fear of a hefty price tag, but upgrading legacy solutions does not need to break the bank. Data strategy consulting<\/a> from a service provider like Intellias can help you tap into your bank\u2019s data and align digital strategies with business goals.<\/p>\n Banks that are ready to begin their data-driven journey can take these steps to ensure success.<\/p>\n You\u2019re data-driven, but are you getting everything out of your data? Intellias offers data & analytics services to help you do more with your data.<\/p>\n Intellias is highly experienced in developing banking technology<\/a>. This includes data engineering and system integration to create a strong digital architecture that keeps data points secure.<\/p>\n Intellias has developed many financial solutions<\/a> including payment systems, mortgage platforms, personal finance software, mobile cash payment solutions, risk management systems, Apple Pay and Google Pay service links, and software as a service (SaaS) platforms. Here are some ways we\u2019ve helped financial institutions achieve data-driven success.<\/p>\n A global financial solutions provider that manages retirement assets wanted to move to a more efficient cloud-based infrastructure<\/a>. The company wanted to accelerate and improve the quality of data processing by enhancing their data processing environment with the help of Microsoft Azure. Although they already were using Azure, they were not taking advantage of all its capabilities.<\/p>\n Intellias engineers carefully chose software while keeping data availability in mind:<\/p>\n In the end, the company was able to process data better, with a smoother flow of data and accelerated data processing. The new cloud environment could consume and process files in various formats, such as Salesforce system<\/a> components, and provide them to many users. The financial provider also became more resilient to processing errors.<\/p>\n When a major financial institution with more than 2.5 million customers needed to migrate sensitive banking data<\/a>, they chose Intellias to help. The bank\u2019s old systems were on-premises and considered high-risk for business continuity. They could not meet demands, lacked resiliency and were not scalable. These challenges frequently led to downtime and exposed the financial institution to operational risks. As a result, the company decided to migrate to the AWS cloud.<\/p>\n Because of geopolitical factors, we had to migrate the applications on a reduced timeline to minimize risks associated with the disaster recovery plan and satisfy \u201cbusiness as usual\u201d requirements. Our engineers followed advanced AWS DevOps migration<\/a> best practices. We also:<\/p>\n The solution created an environment where departments could collaborate for data-driven decision-making. In just two and a half months, Intellias migrated online banking and financial services to an AWS private cloud.<\/p>\n We helped a telecommunications company create Germany\u2019s first mobile-only banking system<\/a>. The Banking as a Service (BaaS) model was designed to offer fast, secure and user-friendly banking solutions.<\/p>\n Our engineers used a microservices architecture in Ruby on Rails to integrate with the bank\u2019s existing infrastructure. Among other solutions, the custom app<\/a> includes:<\/p>\n The app also included a unique feature: for every euro spent using their cards, customers received 1 MB of high-speed data. PINs and fingerprint logins offered additional protection.<\/p>\n Intellias engineers developed the first digital-only banking system in Germany<\/a>, which offers investment banking and online-only payment methods in addition to traditional retail banking services. Supported payment methods include crowdfunding, social lending, multi-currency eWallets and cryptocurrency (Bitcoin, Litecoin, Ethereum, Dash and other altcoins). The banking system also supports direct money transfers to Facebook friends and phone contacts. Furthermore, it accepts contactless smartphone payments. Investors can store virtual currency as securely as cash.<\/p>\n Our client has a rapidly growing base of customers, of which 20% are full-service banks. Integration with Facebook, Twitter, eBay, Xing and other networks has driven demand for the client\u2019s digital banking services.<\/p>\n To meet customers\u2019 expectations, we created three dedicated teams.<\/p>\n The expected result is a robust, scalable and customer-friendly digital banking platform that puts our client at the forefront of digital banking.<\/p>\n\n
Why are banks investing in data, analytics and AI?<\/h2>\n
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What are the challenges to becoming a data-driven bank?<\/h2>\n
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<\/p>\n5 steps to becoming data-driven<\/h2>\n
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Bank modernization success stories<\/h2>\n
Managing Azure cloud services<\/h3>\n
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Engineering an AWS ecosystem<\/h3>\n
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Developing a mobile banking system<\/h3>\n
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Designing a core banking system<\/h3>\n
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Conclusion<\/h2>\n