The days of making decisions using printed reports with a coffee cup ring have passed, but many financial institutions have not kept up with modern data-driven banking technology. Legacy banking systems are decades old and operating past their life expectancy. Meanwhile, today’s digital banking industry requires fast, actionable insights from high-quality financial data analytics, which includes customer data signals, transaction histories and possible threats. As a result, banks are investing heavily in big data and technology.
These new digital solutions (including cloud storage, artificial intelligence (AI) and machine learning (ML), a subset of AI) help banks in many ways. For example, they can predict financial needs based on a customer’s spending history, improve operational efficiency by automating manual tasks, and prevent fraud by analyzing transaction patterns. New technology also helps banks have a better foundation for managing and analyzing financial data, comply with increasing financial regulations, improve customer satisfaction and generate stronger profits.
Make data-driven decisions like the pros. Learn how major global banks use data analytics.
What is data-driven banking?
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 says the banking sector could achieve $200–$340 billion in value by taking advantage of all generative AI capabilities.
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’s financial history or projected future spending.
There are many benefits to becoming data-driven. Data and analytics allow banks to:
- Eliminate inefficiencies by automating manual processes
- Assess risk of credit with greater precision
- Detect fraud by analyzing large volumes of real-time transactional data and instantly flagging suspicious activity
- Ensure compliance with changing industry regulations
Why are banks investing in data, analytics and AI?
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.
Benefits banks expect from data and analytics solutions
Source: Tableau and FinTech Futures, The Power of Data Analytics in FinTech Solutions
Here are several benefits banks expect from AI, data and analytics:
- Cost reduction: Banks significantly reduce operational costs when they automate routine processes — for example, check compliance, approve loans and monetize claims. McKinsey reports that operations consume 15-20% of a bank’s budget.
- Customer demands: Consumers have come to expect more from their banks than traditional, one-size-fits-all products. For a personal touch, banks use AI-powered systems to store and analyze transaction histories, look at spending patterns and review personal goals. Banks then make recommendations based on the data. For example, a customer might get pre-approval offers for a car loan when transactions indicate high car repair costs.
- Fraud prevention: ML models can analyze millions of real-time transactions. They then can detect unusual patterns in the data and alert about a potential threat.
- Competition: As newer digital banks adopt consumer-friendly FinTech solutions, traditional banks must invest in new systems to keep up.
What are the challenges to becoming a data-driven bank?
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 system eliminates those silos and makes data available to the entire organization for daily decision-making.
Source: McKinsey & Co.: The data and analytics edge in corporate and commercial banking
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.
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.
Where do banks stand on data?
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.
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 from a service provider like Intellias can help you tap into your bank’s data and align digital strategies with business goals.
5 steps to becoming data-driven
Banks that are ready to begin their data-driven journey can take these steps to ensure success.
- Assess existing data: Evaluate the bank’s existing data infrastructure and capabilities. Note where there are data silos, suboptimal data quality and technical inefficiencies.
- Apply data governance strategies: Establishing clear policies and practices for the use of data analytics helps banks maintain data integrity and comply with regulatory requirements.
- Invest in analytics and AI: Machine learning models analyze large volumes of information, identify trends in data and make real-time decisions.
- Create a data-driven culture: Employee buy-in is essential for a data-driven culture. Banks should be prepared to provide training, leadership support, and clear communication about the benefits of data-driven strategies.
- Build scalable systems: Cloud-based platforms and other services provide flexibility to grow as your bank’s level of data maturity improves.
- Choose a partner for the journey: Banks must choose an experienced partner to help them bring their legacy systems into the next generation of digital banking.
Bank modernization success stories
Intellias is highly experienced in developing banking technology. This includes data engineering and system integration to create a strong digital architecture that keeps data points secure.
Intellias has developed many financial solutions 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’ve helped financial institutions achieve data-driven success.
Managing Azure cloud services
A global financial solutions provider that manages retirement assets wanted to move to a more efficient cloud-based infrastructure. 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.
Intellias engineers carefully chose software while keeping data availability in mind:
- Databricks replaced an Azure SQL database for advanced data analytics
- Snowflake data marts were added for a real-time repository that offered dynamic access to business intelligence (BI) and data analytics in banking solutions
- An Azure data lake improved flexibility in the size, format and speed of data
- Azure Functions provided an event-driven architecture
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 components, and provide them to many users. The financial provider also became more resilient to processing errors.
Engineering an AWS ecosystem
When a major financial institution with more than 2.5 million customers needed to migrate sensitive banking data, they chose Intellias to help. The bank’s 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.
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 “business as usual” requirements. Our engineers followed advanced AWS DevOps migration best practices. We also:
- Took an Infrastructure as Code approach with Terraform, Terragrunt and Atlantis, which helped the client build a robust and scalable ecosystem
- Used other third-party tools such as PaloAlto FW, CloudFlare and Dynatrace to increase security, provide monitoring and improve performance of the cloud systems
- Automated testing, deployment and use of data at scale to meet industry standards for resilience and performance
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.
Developing a mobile banking system
We helped a telecommunications company create Germany’s first mobile-only banking system. The Banking as a Service (BaaS) model was designed to offer fast, secure and user-friendly banking solutions.
Our engineers used a microservices architecture in Ruby on Rails to integrate with the bank’s existing infrastructure. Among other solutions, the custom app includes:
- Private online accounts, in-app Mastercard/Visa services, payment processing, money transfers and transaction tracking
- A 60-second P2P instant transfer feature that lets bank customers quickly transfer money to their phone or email contacts
- Real-time analytics and the ability to categorize expenses so users can get insights into their spending habits through a personal financial management system
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.
Designing a core banking system
Intellias engineers developed the first digital-only banking system in Germany, 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.
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’s digital banking services.
To meet customers’ expectations, we created three dedicated teams.
- One team developed a back end for the digital banking system to support cash withdrawals, card top-ups at supermarket checkouts, contactless NFC payments, international card use and secure online purchases.
- The second team is creating a web solution for a global mobile operator, one of the digital bank’s partners. This solution is expected to extend the bank’s web presence and complement existing applications with a new API.
- Team number three is developing a data platform that lets end users effectively aggregate and store many data assets from different sources, including external sources. It then rapidly processes the data for future service customization.
The expected result is a robust, scalable and customer-friendly digital banking platform that puts our client at the forefront of digital banking.
Conclusion
The future of banking lies in AI, big data and analytics, which can be used for data-driven customer insights for banking and many other use cases in banking. Institutions that embrace their digitalization journey are better equipped to adapt to changing customer expectations, meet regulatory demands and maintain a competitive edge. New data technologies can help banks make operations more efficient, reduce risks, and deliver personalized services that enhance customer satisfaction. Data-driven decisions will give you an edge in today’s banking industry. But you still might not want to put your coffee cup on the annual report.
Are you looking to upgrade your core banking system or move your financial data to the cloud? Intellias has many options to replace legacy systems and democratize your legacy systems. If you don’t know where to start, let Intellias guide you.
Contact us to develop powerful solutions that lead to data-driven banking decisions.
