BFSI
Retail & Customer Banking
CASA attrition and cross-sell opportunity both leave a trail in transaction and product-holding data long before they show up as a closed account or a missed campaign target — but that trail is scattered across core banking, CRM, and campaign systems that rarely get queried together.
FireAI connects these systems to flag attrition risk while there's still time to retain the customer, rank cross-sell propensity by segment, and track complaint resolution and lifetime value at the branch level — so retail banking leaders can act on the signal instead of the lagging report.
CASA Growth & Attrition Analysis
Current and savings account attrition is usually discovered when the closure request reaches the branch — by which point the relationship manager has no window to intervene. The balance-decline and transaction-drop pattern that precedes closure is sitting in core banking data, unwatched.
FireAI monitors balance trend and transaction velocity per account to flag early attrition risk weeks before closure, ranked by account value, so relationship managers get a prioritized retention list instead of finding out after the fact.
CASA Growth Dashboard
Cross-Sell Propensity by Customer Segment
Cross-sell campaigns are usually run as blanket offers across a segment, because building a propensity model requires stitching transaction behavior, product holding, and demographic data that sit in separate systems.
FireAI combines transaction patterns, existing product holding, and account tenure to rank customers by propensity for a specific product, so cross-sell campaigns target the customers most likely to convert instead of the entire segment.
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Branch-wise Customer Complaint Resolution TAT
Complaint resolution time is usually reported as a branch or regional average, which hides the specific complaint categories or branches where turnaround is quietly breaching internal SLA.
FireAI breaks down resolution TAT by branch and complaint category, flagging categories trending toward SLA breach before the monthly average masks the problem.
Customer Lifetime Value & Product Penetration
Product penetration per customer is usually tracked as a static count, without connecting it to the revenue or margin each product relationship actually contributes — making it hard to prioritize which penetration gaps are worth closing first.
FireAI combines product holding with margin contribution per product to rank customers by lifetime value and surface the highest-value penetration gaps, so relationship teams prioritize the conversations that matter most.