How can you integrate data from multiple sources to get a complete view of your bank's operations?

Bank data integration offers several benefits that can enhance operational efficiency, decision-making, and customer satisfaction. In my experience, integrating data from various systems allows banks to create 360 degree view of customer, advanced and real-time risk management, cross-selling opportunities, smart insights, customer stickiness, retention are some of the additional benefits.

? Enhanced Customer Experience: Consolidated data allows you to tailor products and services to individual customer needs. ? Improved Operational Efficiency: Data integration streamlines processes by eliminating manual data entry and reconciliation across different systems. This reduces errors, reduces risk and frees up resources for higher-value tasks. ? Data-Driven Decisions: A unified view empowers informed decision-making. You can analyse trends, identify risk factors, and optimise resource allocation better with a comprehensive data set at your fingertips that is shared across multiple departments and disciplines. ? Regulatory Compliance: Centralised data makes meeting financial regulations more manageable.

Data integration in the banking domain has several challenges due to disparate and complex banking systems, regulatory requirements, data privacy needs, and real-time data need for better customer experience. Core banking systems often have data silos and they store data in different formats. This creates the challenge due to inconsistency, lack of SME knowledge, data quality, missing lineage. Due to highly regulated industry, adhering to security standards such as GDPR, CCPA, KYC, AML requires careful governance as well.

The management of a hugely complex stack of multiple products from multiple vendors, all requiring different skill sets, is costly and unwieldy. Changes from shifting regulatory requirements, capitalising on market opportunities or reacting to risks demand the implementation of strict change management processes. Project managers get involved, sprints, scrum masters and a whole gamut of bureaucracy that impedes the bank's agility to pivot and respond to those risks or opportunities. There are obvious costs associated with this, operational costs, whilst not forgetting the costs of lost opportunity.

Put all data in a common scale and assign weights to each risk (highest to lowest). Monitor for changes over time to see which risks increase or decrease. Don't forget to update the indicators over time.

In my experience having comprehensive data integration strategy that includes data governance, data quality, metadata management, Integration technology and tools (such as API and Event based integration) under one bank technology org and strategy must align with business objectives, regulatory requirements, IT infrastructure (On prem or cloud). This would empower banks to derive actionable insights, improve operation efficiency, and enhance customer experiences in the digital banking landscape.

This is where your defined data architecture becomes important. By choosing tools from a common family or from one suite of tools, you can reduce the number of moving parts in your technology stack, reduce the required skill sets, increase agility, and dramatically reduce costs. ?Define Your Goals: Clearly outline what you want to achieve with data integration. ?Standardise: Establish consistent data formats and definitions across all systems. This ensures seamless integration and accurate analysis. ?Invest in Data Governance: Implement policies to ensure data quality, security, and accessibility ?Choose the Right Tools: Don’t overcomplicate things. Consider cloud-based platforms like TotalView from InterSystems for scalability and agility

Build your aggregations and transformation rules, but leave the source data where it is. Bring the query to the data, not the data to the query. The modern and latest paradigm that embraces these concepts in architecture is a data fabric which is highly endorsed by independent analysts such as Gartner and alike. Data fabrics are built using tools and services to provide a common set of services holistically. There are even tools that come out of the box as a data fabric, such as TotalView? from InterSystems, that connect, collect, integrate and harmonize data from both within and beyond the enterprise, serving it up in a self-service manner to analysts, quants or data scientists from an easy-to-deploy scalable cloud service.

Start with defining objectives of data integration initiative, list down use cases, leverage design thinking process to define use cases (such as customer experience, improving lending cycle time, money movement, risk management). Identify all the data sources, establish data governance process, ensure data security guidelines requirements are on the top of the list, select integration technology and tools, design data integration architecture, execute the program, leverage development and testing automation, implement CI/CD pipeline, deploy to production, monitor and iterate.

Yes to get holistic view of customer profile, multi source data is needed to the do the profiling & perform personalization on those data set. It's one of ml based recommendation of products based on customer profile.

Data integration can be trigger new opportunities and solving complex market problems but in services industry specially banking sector which have vast variety of clients and customer. It can be only possible by right market segmentation such as age , sex , business and business market.