Proving AI ROI in Financial Services with Agentforce

Financial institutions are under relentless pressure to cut costs, improve risk decisions, reduce operational friction, and deliver personalized customer experiences — all while navigating tightening margins and regulatory scrutiny. The promise of artificial intelligence is real, but only if it delivers tangible, quantifiable return on investment (ROI).

This is where TELUS Digital’s Salesforce implementation expertise (especially with Agentforce) becomes a strategic advantage. Below is a results-oriented framework that separates aspiration from performance and shows exactly where AI is delivering measurable business outcomes.

Cut operational costs and improve efficiency

Financial institutions continue to operate with cost structures built for manual, people-intensive service models that do not scale. Contact centers, servicing teams, and back-office operations are overwhelmed by high-volume, low-complexity interactions that add cost without adding value. As volumes grow, institutions are forced into a false choice: increase headcount or degrade customer experience.

AI can automate the handling of routine financial services inquiries across channels 24/7, reducing call volumes and freeing agents for higher-value tasks.
Salesforce reports that Agentforce’s digital labor platform is now used by over 12,000 clients, generating $100M+ in annual efficiency savings and uncovering previously missed business opportunities.

Operational automation isn’t about “productivity gains”, but rather reducing recurring expense lines and improving agent utilization in measurable terms.

Reduce losses through better fraud and risk detection

Fraud losses are increasing in both volume and sophistication, while traditional rules-based systems struggle to keep pace. These systems generate excessive false positives, wasting investigator time and frustrating legitimate customers through unnecessary transaction declines. The result is a compounding problem: higher losses, higher operating costs, and eroding customer trust.

This is precisely where adaptive, AI-driven detection models are producing defensible loss-reduction ROI.

AI systems used for fraud detection can cut detection costs by up to 60% and improve accuracy to ~96%, reducing overall loss exposure.
According to broader industry data, roughly 60% of financial institutions are using AI for fraud detection, and expected adoption in customer service alone could handle ~50% of interactions via AI by 2025.

AI agents can act as persistent risk monitors, evaluating anomalies and transaction patterns in real time without human intervention. This reduces the time to detect fraud and limits loss exposure while improving compliance posture.

Improve underwriting and risk assessment

Legacy underwriting models rely on static scorecards and backward-looking data, limiting their ability to assess real-time risk. This leads to conservative approval strategies that suppress revenue or overly permissive decisions that increase default exposure. In both cases, institutions sacrifice margin due to poor risk precision.

Industry performance data makes clear how AI materially improves risk accuracy without increasing exposure. While specific Agentforce numbers aren’t publicly disclosed yet, industry studies show that AI-driven risk models can improve accuracy of risk assessment by 40–60% and deliver significant ROI after the 18 months when fully adopted.

Better risk engines shorten decision cycles, reduce credit losses, and expand profitable lending without increasing risk, and that’s a direct P&L impact.

Enhance customer experience with 24/7 personalization

Customer expectations are now shaped by real-time, always-on digital experiences outside financial services. Yet many institutions still rely on fragmented systems that prevent agents from seeing a complete customer view, resulting in repetitive, impersonal interactions. This disconnect directly contributes to churn, lower satisfaction scores, and missed revenue opportunities.

The following metrics show how AI-driven personalization converts experience improvements into measurable revenue and retention gains.

AI handling of customer service queries is increasingly accurate (i.e. reported industry use above 90% accuracy for automated responses).
Broader statistics show financial firms using AI for customer service can realize ~40% cost savings and 25% improved satisfaction scores.

Agentforce builds contextual, industry-specific AI agents that integrate directly with Salesforce data — meaning responses reflect the customer’s real history, current products, and relationship insights, not generic AI outputs.

Personalized support drives higher retention and wallet share, which is measurable through churn rates and cross-sell/up-sell metrics.

Reduce compliance risk and improve regulatory reporting

Compliance teams are under pressure to monitor exponentially growing volumes of transactions, communications, and customer activity. Manual reviews and sampling approaches are expensive, slow, and increasingly ineffective at identifying emerging risks. When issues are missed, the financial and reputational consequences can be severe.

This is where AI-enabled monitoring and automation demonstrably reduce cost while strengthening regulatory posture. AI can proactively monitor communications, transactions, and patterns that indicate risk, often reducing false positives that bog down compliance teams. Industry adoption data shows widespread use of AI for enhanced monitoring.

Because Agentforce is built on Salesforce’s trusted AI architecture and integrates with enterprise data governance tools such as Data 360, institutions can automate compliance workflows while retaining visibility and control.

The data and process foundation that makes Agentforce deliver ROI

Salesforce Salesforce Financial Services Cloud is the system of record that turns AI from an isolated capability into an operational advantage. Financial institutions struggle with fragmented customer data spread across core systems, servicing platforms, and legacy CRMs, which limits their ability to act in real time. Financial Services Cloud consolidates client profiles, household relationships, products, interactions, and lifecycle events into a single, governed data model purpose-built for banking, wealth, and insurance.

This unified foundation is what allows Agentforce to operate with precision rather than generic automation. Instead of responding with scripted or probabilistic answers, Agentforce draws from real customer context—account history, policies, cases, transactions, and service entitlements—to take action inside existing workflows. That means AI agents can resolve service requests, triage cases, surface risk signals, and recommend next-best actions while remaining compliant with institutional rules and regulatory constraints. The result is AI that is embedded in day-to-day operations, not bolted on as a front-end experiment.

When implemented correctly, Financial Services Cloud and Agentforce together create a closed-loop system: data informs decisions, decisions trigger actions, and outcomes continuously refine performance. This is where TELUS Digital delivers differentiated value—aligning data models, process design, and AI governance so institutions can scale automation without sacrificing control. The payoff is measurable: faster service resolution, lower cost-to-serve, improved risk visibility, and AI-driven outcomes that can be tracked directly back to financial KPIs.

Maximizing AI ROI with TELUS Digital

AI isn’t a plug-and-play widget. It fails when it doesn’t align with data, process, and governance. That’s where implementation strategy matters:

Unified data foundation: successful AI depends on clean, governed, comprehensive customer data.
Process redesign: AI must be integrated into existing workflows, not bolted on.
Governance and control: regulated industries must maintain visibility into how AI decisions are made.

TELUS Digital brings deep expertise in Salesforce Financial Services Cloud, Data 360, and Agentforce integrations, ensuring AI is both trusted and tied to measurable KPIs, not experimental pilots.

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