Who this applies to: Leaders responsible for finance/accounting, risk management, compliance, operations, IT, and the C-suite at financial institutions.
The FMS East Coast Regional Conference recently brought financial professionals together in Annapolis, Maryland, for three days of learning, discussion, and networking. During the conference, we presented “The Future of Finance: How AI and Tech Can Transform,” a session focused on how artificial intelligence and automation are changing finance, accounting, risk, and operations within financial institutions.
Our central message was straightforward: Financial institutions will not gain a lasting advantage simply by using AI. As these tools become more widely adopted, differentiation will come from how effectively leaders redesign processes, develop their people, strengthen governance, and improve decision-making.
Finance is moving from reporting to anticipating
Historically, many finance functions have concentrated on periodic reporting, manual reconciliations, historical analysis, and responding to issues after they occur. The emerging model is more continuous and forward-looking, with real-time visibility, automated monitoring, predictive insight, and proactive decision support.
AI may help financial institutions move routine processing into the background while allowing finance professionals to spend more time interpreting information, advising leadership, and applying judgment. Potential applications include:
- Reconciliations
- Regulatory change monitoring
- Board-package preparation
- Vendor due diligence
- Internal audit planning
- Fraud detection
- Policy summarization
- Credit and liquidity analytics
Importantly, AI is not the same as traditional automation, which generally follows predetermined rules and structured inputs. AI-enabled solutions can work with structured and unstructured information and may generate, predict, summarize, or recommend. Those capabilities create meaningful opportunities, but they also introduce new risks.
Innovation must be matched with governance
Institutions considering an AI use case should ask several questions at the outset:
- Is the use case material?
- Who owns it?
- What data will the tool use?
- Could the output affect customers or members?
- What controls and monitoring will be necessary?
- What vendor risks are involved?
AI may introduce risks involving data quality, inaccurate or fabricated outputs, bias, cybersecurity, privacy, model drift, third-party vendors, overreliance, and governance. Addressing these risks begins with clearly defined ownership, appropriate board visibility, acceptable-use policies, approved tools, data restrictions, training expectations, and a documented risk assessment for each use case.
One of the most important principles from the session was to treat AI-generated outputs as assertions requiring evidence, not as facts. Depending on the use case, controls may include human review, performance testing, segregation of duties, source citations, third-party validation, exception management, and evidence retention.
What the case studies revealed
During the session, attendees worked through scenarios involving AI-assisted reconciliations, AI-generated board reporting, and regulatory change monitoring.
In the reconciliation scenario, an overly broad matching threshold caused the system to clear items incorrectly. In the board-reporting scenario, AI-generated commentary relied on an outdated ALCO assumption. In the regulatory-monitoring scenario, an incomplete source library caused the institution to miss an important update.
Although the scenarios involved different processes, the lessons about what matters were consistent, including:
- Data sources
- Human review
- Evidence
- Ongoing monitoring
The objective is not to slow innovation. It is to make innovation reliable.
Start with the process, not the technology
Institutions do not need to transform everything at once. A practical approach is to:
- Establish governance
- Inventory current use cases
- Assess risks
- Design controls
- Implement and communicate expectations
- Monitor and adapt
Before selecting a tool, leaders should map the existing workflow, identify bottlenecks and manual workarounds, locate sources of error or delay, and clarify ownership. Opportunities can then be prioritized based on business impact, frequency, risk reduction, feasibility, and data availability. A controlled pilot allows the institution to measure results, refine controls, and scale intentionally.
For many institutions, practical starting points may include reconciliations and exception management, board-package summarization, internal audit risk assessment, regulatory change monitoring, and vendor contract review. More complex applications, including fraud detection, credit risk analytics, CECL forecasting support, liquidity forecasting, and continuous auditing, may provide significant value but require more deliberate governance. Autonomous approvals or high-impact customer decisions without meaningful human review should be approached with particular caution.
The future finance function will not remove people from the process. Instead, technology will perform more routine tasks while people remain responsible for judgment, ownership, challenge, and decision-making. The institutions best positioned for that future will be those that start thoughtfully, build governance into their initiatives, and scale only after they understand both the benefits and the risks.
Key takeaways
- Focus on process improvement, not just technology adoption, because long-term value from AI comes from better workflows, stronger governance, and more informed decision-making.
- Shift finance teams from historical reporting toward real-time insights, predictive analysis, and proactive decision support.
- Establish governance before scaling AI initiatives, including clear ownership, risk assessments, usage policies, data controls, and monitoring.
- Verify AI-generated outputs through human review and supporting evidence, treating AI results as assertions that require validation rather than fact.
About BerryDunn
Have a question about how your financial institution can thoughtfully assess its processes, train team members, create effective internal controls, and improve decision-making to maximize the value of AI? Please don’t hesitate to reach out. Learn more about our team and services.