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Banking Data Analytics
Software Features, Costs, and Benefits
With 34 years in data analytics and 18 years in developing IT solutions for banking, ScienceSoft builds custom analytics software that helps banks gain a 360-degree view of their processes, customers, and assets.
Contributors
Head of Data Analytics Department, ScienceSoft
Banking IT Consultant, ScienceSoft
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Banking Data Analytics: the Essence
Banking analytics is needed to consolidate and analyze diverse banking data, including sales, loans, investments, product and service portfolios, and customers. Banking analytics solutions allow banks to increase their wallet share, enhance customer loyalty, maintain regulatory compliance, manage risks, detect fraud, boost marketing effectiveness, and more.
Integrations: Core banking system, banking CRM, ERP, client-facing apps, fintech software, banking operations management system, accounting software, and more.
Implementation costs: $100,000–$1,500,000, depending on the solution’s scope and complexity.
ROI: up to 415% with a payback period of 6 months.
Banking Analytics: Key Features
Below, our experts outline analytics software features that are most frequently requested by our customers in banking.
Institution’s performance analytics
- Tracking KPIs like operating profit and return on assets (ROA).
- Tracking bank stability indicators like liquidity coverage ratio (LCR), Tier 1 capital ratio.
- Performance forecasts and what-if models based on historical data (e.g., financial statements, economic, internal operations, and more).
- Tracking customer-related KPIs (e.g., prospect price elasticity, CSAT, churn rate).
- Automated customer segmentation, e.g., by age, income, preferred products, and industry (for B2B customers).
- Insight into customer sentiment towards services and products based on AI-driven feedback analysis.
- Identifying attrition drivers throughout the customer journey (e.g., support service issues, excessive account maintenance fees).
- AI-powered recommendations on service personalization (e.g., suggesting loan interest rates based on borrower credit score).
Marketing analytics
- Real-time marketing campaign monitoring (e.g., likes and shares on social media, ads click-through rates, email opening rates).
- Continuous monitoring of market trends and competitor activities.
- Customer lifetime value analysis.
- Identifying cross-selling and upselling opportunities based on previous purchases and needs.
- Analyzing the effectiveness of marketing and retention campaigns (e.g., conversion rates, return on marketing investment).
- Dynamic personalization of marketing campaign content based on customer preferences and purchase history.
- Tracking KPIs like operating cash flow, AR turnover ratio, revenue per department, and sales per product.
- Payroll analytics (e.g., employee compensation vs performance analysis).
- AI-driven predictions (e.g., late-to-pay customers, interest rates to pay based on savings account balance).
- AI-powered suggestions for financial management optimization (e.g., optimal capital allocation in a certain market situation).
- Tracking KPIs like average transaction processing time, average customer wait time, and cost per transaction.
- ML-powered identification of operational bottlenecks (e.g., identifying slow digital payments, ATM errors, inefficient employees).
- Monitoring customer and technical support service KPIs (e.g., first response time, average time to resolution, resolution rate).
- AI-powered identification of fraudulent activities (e.g., account takeover attempts, falsified financial statements) with immediate alerting.
Risks analytics
- Identifying optimal credit and liquidity limits.
- Multi-dimensional analysis of customer credit risk profiles (e.g., payment history, credit scores, debt-to-income ratio).
- Continuous monitoring and identification of potentially risk-incurring market events (e.g., changes in currency exchange and interest rates).
- What-if modeling for risk assessment (e.g., currency risks and commodity price risks for hedging strategies planning, VaR, CFaR, EaR, PD, LGD).
- Monitoring transaction-related risks to detect money-laundering activities, transactions from sanctions-affected regions, etc.
- Monitoring the performance of investment portfolios (e.g., securities, bonds) and their regulatory compliance.
Reporting
- Building user-specific dashboards (e.g., for finance teams, marketing specialists, C-suite).
- Zero-code reports with capabilities for slicing and dicing, drilling up and down.
- Compatibility with specific reporting forms, including Basel III.
- Automated report submission to regulators.
Implement Banking Analytics with ScienceSoft
ScienceSoft’s data analytics consultants, solution architects, software developers, and compliance experts are ready to assist you in building a reliable banking analytics solution that will fit your unique goals and the specifics of your bank.
Essential Integrations for Banking Analytics
Core banking system
- To continuously monitor financial transactions (e.g., payment, money transfer, lending, investment, insurance activities) and identify patterns in them.
- To detect fraud.
- To control and improve employees’ efficiency.
- To improve customer service request management.
- To get a comprehensive view of operational performance and enable its informed improvement.
Accounting or treasury system
- To analyze financial performance and take measures for its improvement.
Client-facing apps
(e.g., mobile banking, payment, money lending apps)
- To understand customer sentiment toward the provided services/products and personalize the offerings.
Security and compliance tracking tools
- To ensure complete adherence to regulatory requirements and maintain a secure banking environment.
Financial data marketplaces
- To get a complete view of the market situation.
- To get data for what-if modeling and forecasting.
Credit rating bureaus
(e.g., Experian, Equifax)
- To simplify the procedure of checking borrower creditworthiness.
- To enable automated financial report submission.
The optimal approach to integration depends on the existing software ecosystem and its architecture. For example, if the core banking system is already integrated with most of the systems (e.g., payment automation, loan processing, underwriting automation, CRM), a logical and cost-effective option would be integrating the analytics software with the banking system. However, if the data sources are disparate or are legacy, hard-to-integrate solutions, the best way is to integrate them directly into the analytics software via APIs.
How ScienceSoft Builds Reliable Banking Analytics Software
Case-specific analytics
We implement custom software features to address the unique needs of different bank types. For instance, a retail bank would benefit from cross-selling analytics, while commercial and investment banks may need scenario analysis (e.g., for credit risk or trading strategies modeling).
User-centric reporting
We create dashboards tailored to specific user responsibilities (e.g., real-time alerts for compliance officers, drill-down capabilities for customer service professionals) and build custom reports compliant with regulatory forms (e.g., Basel III) to streamline reporting to the relevant authorities.
Guaranteed solution security
We implement role-based user access to prevent the loss of sensitive information, audit trails to track user activity and identify suspicious actions in a timely manner, and build highly secure APIs to guarantee safe data exchange between the integrated systems.
Full regulatory compliance
We enable functionality to support compliance with all the required global and local regulations. For example, transaction monitoring and suspicious activity detection can be implemented to support compliance with AML requirements.
Costs and ROI of Banking Analytics Software
The cost of implementing a banking analytics solution may vary from $100,000 to $1,500,000, depending on software complexity. The major cost factors include data volume and diversity, the number of required integrations, and the need for big data analytics and ML/AI capabilities.
On average, data analytics in banking brings a 3-year ROI of up to 415% with a payback period of 6 months. The ROI drivers include improved customer retention, increased wallet share, and revenue growth associated with efficient cross-selling.
$100,000–$300,000
A basic solution that:
- Enables KPI tracking across 1–2 analytics areas, e.g., financial and marketing metrics.
- Integrates with 1–2 key data sources, e.g., a core banking system.
- Enables batch data processing (e.g., every 12 hours).
- Ensures scheduled and ad hoc reporting.
$300,000–$600,000
A solution of medium complexity that:
- Enables KPI tracking across multiple business areas: finance management, employee performance, customer management, etc.
- Integrates with 3–7 data sources, both corporate and external.
- Features batch and real-time data processing.
- Enables diagnostic and predictive analytics via non-neural-network ML models.
- Provides automated reporting capabilities.
- Enables automated reporting to regulators.
$600,000–$1,500,000+
- Monitors the entire scope of metrics, including market performance.
- Integrates with multiple back-office and third-party systems, including blockchain-based fintech software.
- Enables real-time big data analytics for instant KPI calculation and event monitoring (e.g., for financial fraud detection).
- Provides advanced root cause analysis and forecasting using machine learning.
- Offers AI-supported optimization and personalization recommendations.
- Enables automated generation of complex reports (e.g., consolidated reports, reports compliant with local banking regulations).
- Enables custom reporting in compliance with the established reporting forms like Basel III.
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The Benefits of Analytics for Banking
Below, you can see the results of a Forrester survey illustrating the influence of mature data analytics practices on business outcomes in different domains, including banking. The BFSI sector has observed the most significant benefits and cost savings among the survey participants.
Consider ScienceSoft to Build a Reliable Banking Analytics Solution
Consulting on banking analytics
We are ready to assist you in developing analytics software from scratch or improving your existing system. We’ll provide you with a feasibility study, costs, and ROI estimates, build features specific to your bank type, design architecture, and pick tools and techs optimal for the case.
Implementation of banking analytics
Our team can build a secure, fault-tolerant, and compliant analytics solution and integrate it with the required systems, including legacy software. We’ll develop an intuitive UI and user-specific dashboards to make sure your employees easily get insights relevant to their roles and arising challenges.
About ScienceSoft
ScienceSoft is an IT consulting and software development company headquartered in McKinney, Texas. Since 1989, we help organizations in 30+ domains build analytics solutions that allow companies to benefit from complete visibility into their business processes, timely informed decisions, and proactive risk management. Being ISO 9001 and ISO 27001-certified, we can guarantee top software quality and complete security of our customers’ data.