Financial Firms Expand AI, Lag on Retention

Financial services firms have adopted AI across more tasks than healthcare or media, leading on 27 of 75 AI-supported tasks, according to a PYMNTS Intelligence survey of 60 senior tech executives. While 85% of financial firms plan to increase AI budgets, only 30% use AI for churn prediction and retention, highlighting a gap between investment in revenue/risk and customer retention.

Financial Firms Expand AI, Lag on Retention According to a PYMNTS Intelligence report based on a March 2026 survey of 60 senior technology executives at U.S. enterprises with at least $1 billion in annual revenue, financial services firms have implemented AI across more tasks than healthcare or media. The report finds financial services lead adoption on 27 of 75 AI-supported tasks, compared with 16 in media and advertising and 10 in healthcare, per PYMNTS. The research also reports that 85% of financial services and insurance firms plan to increase AI budgets over the next 12 months, but only 30% use AI for churn prediction and retention targeting, according to PYMNTS. Editorial analysis: This gap between heavy investment in revenue/risk and lower investment in customer-retention use cases highlights data, orchestration, and measurement challenges for practitioners building retention systems. What happened According to a PYMNTS Intelligence report based on a March 2026 survey of 60 senior technology executives at U.S. enterprises with at least $1 billion in annual revenue, financial services firms are using AI on more tasks than healthcare or media, per PYMNTS. The report states financial services reached high adoption on 27 of 75 AI-supported tasks, versus 16 in media and advertising and 10 in healthcare, according to PYMNTS. PYMNTS also reports that 85% of financial services and insurance firms intend to increase AI budgets over the next 12 months and that only 30% of financial firms use AI for churn prediction and retention targeting. Editorial analysis - technical context Industry-pattern observations: enterprises often adopt AI first where workflows are structured, data is tabular, and governance is mature. The PYMNTS data fits that pattern, showing finance applying AI to revenue recognition, credit risk, and sales forecasting at higher rates PYMNTS lists 65% for revenue close and 60% for credit risk and forecasting in its findings . Retention and churn use cases tend to depend on stitched customer histories, event streams, and cross-system identity resolution, which are engineering-heavy rather than model-heavy problems. Context and significance The divergence in use-case focus matters for practitioners because scale in core finance tasks does not automatically translate to reliable customer-retention systems. Building churn-prediction and personalized retention requires end-to-end data pipelines, feature stores or alternatives, real-time scoring, and instrumentation to measure lift. Firms that have optimized AI for forecasting and risk may still need to invest in customer data integration and experimentation to realize similar ROI in retention. What to watch Editorial analysis: Observers should track indicators such as increased budget allocations specifically for customer data platforms, growth in A/B testing adoption tied to AI models, and case studies showing measured lift from retention-focused initiatives. PYMNTS reports a low baseline adoption for retention in finance 30% , so early practitioner wins will likely be reusable templates for other enterprises seeking to extend AI into customer-growth functions. Scoring Rationale The PYMNTS Intelligence survey highlights a notable adoption pattern in finance that matters to practitioners building customer-facing AI systems, but it is a sector adoption snapshot rather than a technical breakthrough. Practice with real Ad Tech data 90 SQL & Python problems · 15 industry datasets Active Search Campaigns by BudgetEasy /problems/sql/active-search-campaigns-by-budget High CPC Clicks & Poor Landing PagesMedium /problems/sql/high-cpc-clicks-poor-landing-page Campaign ROAS by Attribution ModelHard /problems/sql/campaign-roas-by-attribution-model 250 free problems · No credit card See all Ad Tech problems /problems/datasets/adtech