Where AI is Delivering Real ROI in Financial Services
By Christopher Jackson, industry strategy and marketing lead for financial services at Creatio
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Need to Know:
- AI generates measurable ROI across the entire sales and growth lifecycle by improving acquisition, enabling smarter up-sells and cross-sells, and increasing customer lifetime value.
- Service and experience lifecycle operations can see significant cost-to-serve reductions as AI automates routine tasks and assists employees with case resolution.
- Operational workflows become faster and more efficient when AI eliminates much of the manual work, accelerates processes, and reduces operational friction.
Artificial intelligence has long been discussed in financial services, but the industry is now entering a phase where AI is expected to deliver measurable business outcomes. Rising cost-to-serve, increasing competition from fintechs, and growing expectations for digital experiences are pushing financial institutions to prioritize technologies that improve both operational efficiency and customer engagement.
In the past, most AI conversations revolved around experimentation. Institutions wanted to understand what the technology could do. Today, the question is more direct: Where is AI actually delivering value?
Technology investments in financial services have always been closely tied to outcomes. Increased revenue, cost efficiency, or improved customer experience are not optional, they are expected.
Since this is the case, the most successful AI initiatives focus on areas where the outcomes can be measured clearly, consistently, and accurately. These use cases sit within operational and customer-facing processes that generate large volumes of activity each day and touch large teams or multiple parts of an organization.
Three areas stand out as delivering the strongest return on investment: the sales and growth lifecycle, service and experience, and workflow facilitation across internal processes.
These areas all share a few common characteristics that make them particularly well suited for AI applications: they deal with large amounts of data, involve repetitive tasks, and produce measurable business outcomes.
Organizations that focus their AI strategies on these places are already seeing tangible results.
1. Sales and Growth Lifecycle: Acquisition, Expansion, and Lifetime Value
AI can play a role across the entire customer lifecycle. The customer journey all the way from acquisition through long-term relationship growth provides many opportunities to incorporate automation and personalization using AI capabilities.
Financial institutions sit on an enormous amount of data. Account balances, transaction patterns, products and portfolios, digital engagement, and service history all contain valuable signals about a customer’s financial behavior and potential future needs.
A lack of data is not the challenge. The challenge is understanding what that data can tell us and how to use it.
AI helps uncover patterns that are difficult or too time-consuming to identify manually and enables institutions to act on them consistently. AI systems can detect signals that suggest when a customer may be ready for a specific product, when a relationship may be at risk, or when a life event, like retirement or a first-time home purchase, may result in a new financial profile with new or different needs.
Institutions used to rely heavily on general marketing campaigns or manual outreach from sales teams or relationship managers. These approaches can be effective, but they are not very precise and are far from personalized.
Industry research, including studies from Deloitte, indicates that personalized AI-driven customer engagement strategies can increase response and engagement rates by as much as 30%, particularly when AI agents identify the right moment for outreach. They are able to engage people at the right times and when financial decisions are either valuable or necessary.
In practice, this commonly includes:
- Evaluating a customer’s portfolio to identify those most likely to open new accounts or adopt additional products
- Comparing customer behavior and product holdings against similar customer segments to identify gaps or opportunities
- Detecting the absence of commonly held products within comparable customer profiles
- Recommending relevant financial products based on relationship or event-driven triggers, such as:
- Life events, including a customer approaching retirement or a child reaching driving age
- Financial changes, such as a significant increase in direct deposit that may indicate a promotion or increased purchasing power
- Proactively engaging customers who show early signs of churn risk, including:
- Reduced account activity or inactivity
- Changes in primary banking behavior, such as redirecting direct deposits
- Limited relationships, such as customers holding only a single product nearing maturity
- Incorporating sentiment and engagement signals, such as tone during interactions, repeated service issues, or declining engagement, to identify dissatisfaction and trigger timely intervention
The return on investment from AI in the customer lifecycle typically can be seen in three metrics:
- Higher conversion rates
- Increased share-of-wallet (additional products per customer)
- Lower customer churn or reductions in at-risk accounts
Understanding who to target and what the next-best-offer is can improve cross-sell conversion rates by up to 25% compared with traditional campaign approaches. At the same time, churn prediction models can reduce attrition by up to 20% when customers are proactively engaged as soon as they begin showing early signals of leaving or decreased activity. For a mid-sized bank or credit union with hundreds of thousands of customers, even incremental improvements in these metrics can translate to millions of dollars in annual revenue.
Increasing the number of products per customer by even 0.2 or 0.3 per household can significantly increase the account’s lifetime value while also strengthening long-term loyalty through share-of-wallet growth.
2. Service and Experience Lifecycle: Reducing Cost-to-Serve
Customer service centers are typically one of the largest operational cost centers for most financial institutions. These departments manage large volumes of routine service case requests each day, which range from balance inquiries and debit card replacements to fraud disputes and account updates. Historically, these cases required significant manual effort from contact center representatives, service teams, and systems.
AI is changing service operations in two fundamental ways, but the more important shift is economic. These changes directly alter how cost is generated and managed across service operations.
First, AI agents are handling a growing share of high-frequency, low-complexity inquiries across channels. This reduces inbound call volumes and shifts routine interactions into digital channels.
Second, AI is improving employee productivity in more complex cases by summarizing interactions, retrieving context, and recommending next steps in real time.
Cost-to-serve reductions are not driven by a single improvement, but by a combination of three factors:
- Call deflection: fewer interactions require human handling
- Handle time reduction: faster resolution for each case
- Failure demand reduction: fewer repeat contacts caused by unresolved issues or missed interactions
AI impacts all three simultaneously. In many cases, the greatest value comes not from making interactions faster, but from eliminating them altogether. For example, when AI proactively confirms appointments or resolves simple issues before they escalate, it removes the need for follow-up interactions entirely.
This shift is already visible in production environments. AI-assisted service operations are reducing average handling times by 20–40% in high-volume workflows, while virtual agents are deflecting a significant share of Tier 1 inquiries into digital channels. Mature deployments report 30–40% reductions in cost-per-contact.
Bank of America, for example, reports that its virtual assistant has handled more than 3.2 billion client interactions, significantly reducing reliance on human-assisted service channels.
In more operationally complex environments, the impact extends beyond contact centers. In large-scale service operations, shifting to proactive, AI-driven customer engagement has reduced missed appointments by confirming access in advance, eliminating a major source of operational waste and repeat interactions.
Across high-volume environments, even modest reductions in repeat contacts or failed interactions translate into meaningful capacity gains. Many organizations are effectively freeing up the equivalent of dozens of full-time employees by reducing manual coordination and repeat work, without increasing headcount.
3. Workflow Facilitation: Eliminating Manual Tasks Across Financial Processes
The third area where AI consistently delivers returns is by facilitating workflows across key financial services processes.
Many financial processes still depend on manual coordination across teams and systems, including document validation, compliance checks, and case preparation. These activities are time-consuming and introduce delays and inconsistencies. The primary value of AI in these workflows is not just speed, but reliability. Processes that previously depended on individual effort become standardized and repeatable.
These workflows can be time-consuming and error-prone when work moves through several departments or people. Embedding AI into these workflows significantly helps to ensure task completion, process accuracy, and accelerated decision support.
As an example, an AI agent can extract data from financial statements, identification documents, and application forms. It can assemble underwriting packages and summarize large case files while validating application data before it moves to the next stage. These capabilities largely eliminate manual work while reducing the time required to complete complex processes.
The benefits of incorporating AI into these financial processes are typically recognized by:
- Decreased financial process cycle time
- Reduced manual labor hours
- Lower error and rework rates
- Increased operational capacity for growth
In lending and onboarding workflows, AI document processing and validation can cut preparation time in half.
By providing compliance or fraud teams with AI-generated case summaries, these teams can reduce investigation preparation time by several hours per case.
When AI benefits are multiplied across thousands of cases annually, the outcome is substantial savings while also re-directing employees’ focus to higher-value work such as advising customers, building relationships and generating additional business, and risk analysis.
What’s Preventing Financial Institutions from Achieving Full Savings?
Despite clear progress, many institutions are not yet realizing the full cost savings potential of AI. The gap is typically not technical, but operational.
The most common obstacles include:
- Fragmented systems: AI can generate insights, but cannot execute across disconnected platforms
- Assistive-only deployments: tools that inform employees but do not reduce workload
- Limited process ownership: no clear accountability for outcomes or ROI
- Change management challenges: employees are not fully trained or aligned with new workflows
- Data accessibility issues: relevant signals exist but are not available in real time
Institutions that overcome these challenges tend to see significantly higher returns because AI is embedded into execution, not layered on top of existing processes.
Where To Start For the Highest Returns
The most successful AI-focused organizations approach adoption with discipline. They focus on a small number of high-friction workflows where improvements can be measured quickly and clearly.
Rather than attempting to transform every process overnight, institutions should prioritize use cases that combine a few characteristics:
- Manual effort is heavy and repetitive
- Cross-system coordination is common
- Revenue, customer experience, or compliance impact is measurable
Strong executive sponsorshipProcesses that occur frequently and produce clear and measurable outcomes, such as lead conversion rates, case cycle times, or document processing volumes, tend to produce themost reliable ROI measurements.
Successful institutions avoid launching multiple AI agents at once. Instead, they start with a single, economically meaningful workflow tied to a clear executive objective, a defined autonomy level, and a specific business sponsor responsible for outcomes. The initial deployment is intentionally narrow, focusing on processes with clear volume, decision logic, and measurable economics, such as referral management, renewals, high-volume servicing, or structured onboarding. The goal is not immediate transformation but measurable proof of value. By delivering results within a short time horizon, often within a quarter, organizations build credibility, gain cross-functional support, and establish a repeatable model for scaling AI adoption.
Building Trust: Governance and Responsible AI
Governance and trust are essential considerations when embedding AI into financial services processes because financial institutions operate in highly regulated environments where decisions must remain transparent, auditable, and compliant with a complex regulatory system.
A strong governance framework for AI adoption includes:
- Clear model validation and oversight
- Transparent audit trails for AI-assisted decisions
- Human oversight for sensitive processes
- Continuous monitoring of model performance and bias
Clear model validation and oversight are crucial to maintaining control and understanding how AI agents operate behind the scenes. This also includes audit trails for automated actions as well as assistive actions, which enable organizations to fine-tune the skills over time. Robust reporting and dashboards allow continuous monitoring of performance metrics, including the returns generated by agents and their actions. And of course, humans in the loop as needed provide a safety net during the ramp-up period of AI-powered processes.
In many successful implementations, AI is positioned as a capability that augments employees rather than replacing them, enabling teams to focus more on advisory, relationship-building, and complex decision-making.
In fact, institutions that initially position AI as a tool that is there to help employees, not replace them, often see stronger adoption and better results.
A successful (and responsible) implementation of AI maximizes adoption and efficiency while maintaining regulatory compliance and customer trust.
The Path Forward With AI
The financial services industry is moving beyond experimentation into a phase by execution and measurable outcomes.
The organizations generating the highest returns are not those deploying AI broadly, but those applying it to the right operational problems and embedding it directly into workflows. By targeting the sales and growth and experience lifecycles, and internal financial workflows, institutions can generate significant returns while continuing to build the operational foundation needed for broader AI adoption and use cases.
As AI capabilities continue to evolve, prioritizing high-value areas, taking a disciplined implementation approach, and maintaining strong governance will afford financial institutions the ability to truly capitalize on the next wave of innovation.
