Banking | Lucid Sphere AI

Banking Analytics: Key Features

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).

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).

Continuous monitoring of a bank’s compliance with regulatory requirements related to financial management (e.g., Basel III, Dodd-Frank Act, SOX, SEC); data privacy (e.g., PCI DSS, GDPR); consumer protection (e.g., TILA, FCRA); AML and KYC policies.
Alerts on compliance violations.

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.

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.

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.