In our previous article, we introduced deterministic agentic AI as the cornerstone of trust, transparency, and compliance in banking. Now, we take a closer look at deterministic vs non-deterministic agentic AI, examining how these approaches perform in practice through direct comparisons, real-world examples, and practical use cases.

Not all AI agents are built the same, especially in the complex world of financial services. Deterministic agentic workflows, such as those powered by Juris AICraft, operate using predefined decision paths and produce consistent, predictable outcomes. By contrast, non-deterministic workflows, as exemplified by emerging autonomous agents like Manus AI, adopt exploratory strategies and yield variable results. This distinction matters greatly. For critical banking activities like credit underwriting, regulatory compliance (including ESG reporting), and financial advisory services, banks need transparency, accountability, and strict adherence to regulatory frameworks.

In this article, we make the case for deterministic AI workflows, demonstrating why they align best with banking operations, delivering reliability, regulatory compliance, and clarity to satisfy customer demands and audit standards alike.

Why Consistency and Auditability Matter in Banking AI

Financial institutions operate under intense regulatory scrutiny and risk management expectations. Every loan approval or investment recommendation must be defensible and traceable. In 2024, global regulatory fines reached a record-breaking $19.3 billion, underscoring the critical importance of robust compliance frameworks. Notably, banks faced the heaviest penalties, amounting to $3.52 billion, representing 82% of U.S. regulatory fines. This startling statistic underscores the importance of having AI systems whose decisions can be explained and documented on demand. Regulators like the U.S. FTC and those enforcing the upcoming EU AI Act expect “trustworthiness, transparency, and knowledge of how GenAI performs relative to expectations”​. In practice, this means bank CIOs and compliance officers must ensure their AI behaves reliably and within established guardrails at all times.

Deterministic agentic workflows inherently provide such reliability. Because their decision logic and pathways are pre-defined (even if powered by AI components), they produce the same output given the same input, every single time. This consistency makes it easy to explain AI-driven decisions to auditors and regulators – you can retrace the exact steps taken in each case. By contrast, non-deterministic agents may chart a different course each run, influenced by probabilistic AI reasoning or even randomness. The result? Unpredictable workflows and outcomes​, which complicate audits and pose compliance nightmares if an AI agent’s “creative” approach violates a policy unknowingly.

Financial regulators also mandate rigorous risk controls and documentation. With deterministic workflows, banks can embed compliance checks at each step (for example, ensuring a loan decision meets Basel II/III credit risk criteria or ESG disclosure standards before moving forward). Every action (data retrieved, score calculated, decision rendered) is logged in sequence. When a regulator comes knocking, the bank can produce a complete, step-by-step audit trail by design. Non-deterministic agents would require capturing a potentially sprawling, ad-hoc chain of reasoning – assuming the agent even “explained” itself well, which is often not the case.

Importantly, customer trust is on the line, too. Clients expect fair and consistent treatment. A deterministic AI will apply the same approved criteria to everyone, whereas a non-deterministic agent might inconsistently interpret criteria or, worse, exhibit biased or erratic behaviour on occasion. According to a McKinsey survey, 70% of AI projects fail to meet their goals due to issues with data quality and integration. These challenges—such as hallucinations (inaccurate data), problematic behaviour (bias and toxicity), and unpredictable workflows (lack of consistency)—are exactly the risks banks cannot afford with customer-facing AI. In an era where one compliance misstep can trigger reputational damage, consistency is the cornerstone of trust.

As the comparison shows, deterministic agentic systems excel at delivering the steady, controlled performance that banks need. Non-deterministic agents, while powerful in theory for exploration, currently come with too many uncertainties for mission-critical banking operations. As Gartner and Forrester have noted in various reports, banks are prioritising AI approaches that they can govern and align with risk management frameworks, exactly the strengths of deterministic workflows.

To illustrate further, consider the flow of a typical loan origination process under a deterministic agentic AI:

Figure: Example of a deterministic AI workflow for loan approval.

In this structured pipeline, the AI agent follows a defined series of steps – from intake of customer application data, to automated credit scoring analysis, to a decision (approve or reject) based on consistent criteria, and finally generating a credit paper report and communicating the outcome. Each stage is executed under preset rules or model guidelines. The predictability of this flow means that if two customers have identical profiles, the AI will always arrive at the same decision and rationale. Such reliability is crucial for fairness and for satisfying auditors that the same rules apply to all applicants. The diagram also highlights where compliance checks or human reviews can be inserted as needed (for example, ensuring the generated credit paper includes all required disclosures before it is sent out). In a non-deterministic approach, by contrast, an autonomous agent might improvise, perhaps pulling different data points in each run or altering how it weighs factors, resulting in potential inconsistency or missing pieces in the report.

Use Cases Where Deterministic Workflows Shine

Let’s examine several banking use cases that particularly benefit from deterministic agentic AI, and how Juris AICraft’s approach aligns with these needs:

Credit and Loan Management

In lending, consistency and speed are paramount. Banks must process high volumes of applications quickly while complying with credit policies and regulations (e.g. fair lending rules). A deterministic AI workflow can handle end-to-end loan processing with minimal variance. For instance, Juris AICraft’s AI Credit Paper solution analyses a borrower’s financial data, risk profile, and collateral, then generates a structured credit assessment in seconds. This mirrors a traditionally manual credit memo process but with far greater speed and consistency. Built for institutional use, the solution is designed to reduce turnaround time, support auditability, and ensure every credit decision adheres to policy, without adding operational complexity.

Critically, deterministic logic ensures that the same underwriting criteria are applied every time. If the bank’s policy says a certain debt ratio or credit score is required for approval, the AI will always respect those rules. This not only avoids accidental policy violations but also makes the outcomes highly explainable: a supervisor or regulator can trace exactly why Applicant A was approved (income, credit score, etc., met the threshold) while Applicant B was declined (a specific criterion was not met), with no arbitrary deviation. Loan approval times can now be reduced from weeks to hours or minutes by using AI agents in workflows​, all while maintaining or improving decision quality. Deterministic agentic systems are a natural fit here because loan decisions must be consistent and defensible. Any exploratory “creativity” from an AI is a risk; imagine an undirected AI agent approving an unusual loan structure one day and rejecting a similar case the next for obscure reasons. That unpredictability is simply not acceptable under banking compliance standards or credit committee expectations.

Furthermore, deterministic AI workflows in credit management can easily incorporate human-in-the-loop checkpoints. For instance, an AI might auto-approve straightforward loans under a certain amount, but route more complex cases to a credit officer. Since the workflow is structured, such conditional steps are easy to define and follow. This ensures that for borderline or policy-exception cases, a human decision-maker can intervene, keeping the overall process compliant and controlled. Non-deterministic agents find this harder to do reliably. If left unchecked, they might either approve something they shouldn’t or bother human reviewers with inconsistent queries. In short, a deterministic approach strikes the right balance: maximising automation and speed in credit processing, while guaranteeing consistency, fairness, and oversight.

ESG Compliance and Reporting

Environmental, Social, and Governance (ESG) compliance has become a key focus for banks and their regulators. Institutions are expected to track and report metrics like the carbon footprint of investments, board diversity, community impact of lending, and more. This is a complex, data-heavy mandate that demands accuracy and uniformity in how information is gathered and analysed. A deterministic agentic workflow is ideal for such tasks: it can be programmed to methodically pull data from approved sources, calculate ESG indicators using standard formulas, and compile reports that adhere to regulatory frameworks (such as SASB or TCFD reporting guidelines) every single reporting cycle.

Consider a use case of an ESG Reporting AI Agent. Such an agent (built on Juris AICraft) could automatically scan through a bank’s loan portfolio and categorise each exposure by industry, geography, and ESG risk rating. It might retrieve emissions data for corporate clients from a trusted database or API, check compliance with sustainability covenants, and then generate an aggregate report for the bank’s disclosures. With a deterministic design, the workflow for data collection and analysis is consistent across every run, ensuring year-to-year comparability and completeness. If a particular data source is missing for a client, the workflow can flag it in a predetermined way (for example, assign a default risk score or alert a human analyst) rather than simply skipping it or making a guess. This level of structured rigour is essential for auditability: regulators or internal auditors can later examine the logs to see exactly which data sources were accessed and how the ESG scores were computed for each asset.

On the other hand, a non-deterministic agent might approach the ESG reporting task by “figuring out” different ways to gather data each time – perhaps using a web search one day, a news feed the next – leading to inconsistent coverage or even the inclusion of unverified information. That unpredictability could result in compliance gaps, like missing a critical metric or relying on a source that isn’t approved by the compliance department. It’s easy to see how that could lead to regulatory penalties, especially as regulators worldwide move toward stricter AI governance. In fact, McKinsey notes that banks are exploring GenAI-powered risk tools that can scan various data (transactions, market news, climate risk data) to inform risk decisions​. But they also caution that strong governance is needed to ensure such AI stays within bounds. By using a deterministic framework, banks can deploy AI for ESG and risk monitoring confident that every required step is executed and documented. This means better risk control and fewer unpleasant surprises when it’s time to present ESG outcomes to the board or regulators.

There’s also a clear efficiency gain. ESG compliance often involves sifting through unstructured content: policy documents, corporate sustainability reports, news articles for controversies, which is labour-intensive for staff. A deterministic AI workflow can be configured to do this automatically: e.g., every quarter it retrieves the latest CSR report from each major borrower, extracts key metrics (using natural language processing trained for this purpose), and updates the bank’s risk database. By doing so in a uniform way, the AI ensures no borrower is overlooked and the same standards are applied throughout. This level of thoroughness, done in a fraction of the time it would take analysts manually, is how banks can keep up with expanding ESG demands without ballooning their compliance teams. And crucially, the AI’s actions are all traceable and repeatable, which is vital for demonstrating to regulators that the bank’s ESG process is robust and trustworthy.

Financial Advisory and Customer Service

When it comes to advising clients, be it retail customers seeking financial guidance or relationship managers assisting corporate clients, AI offers huge potential to augment service. However, advice in finance must be responsible and aligned with regulations (for example, adhering to suitability rules in investment advice, or not over-promising loan terms to customers). Deterministic AI agents can be trusted to operate within these boundaries, making them valuable co-pilots for human advisors or even as automated advisors for simpler tasks.

Imagine an AI-powered financial advisor chatbot that helps retail customers plan their budget, suggest savings plans, or recommend loan products. If this agent is built on a deterministic workflow, it will follow a structured dialogue and only provide advice vetted by the bank’s experts and compliance team. For instance, it might start by gathering standardised information (income, goals, risk tolerance), then use an algorithmic model to suggest a mix of banking products, and finally present the suggestion with the appropriate disclaimers and rationale. At each step, there’s no deviation from what’s allowed: if a certain product is not suitable or a certain statement is not compliant, the workflow simply doesn’t include it. The result is consistent, high-quality advice that a bank can stand behind. Indeed, these systems can be designed to escalate to a human advisor if they detect needs beyond their scope, ensuring a seamless and safe customer experience.

Now consider the alternative: a non-deterministic agent given free rein to “help” a customer. It might generate creative answers using a large language model, possibly pulling in outside information or making assumptions. While this might sometimes delight users, it could also lead to factual errors or even inappropriate recommendations. There have been instances of generative AI agents hallucinating, confidently giving incorrect financial details, or showing subtle biases in advice. In a regulated industry, such mistakes can translate to compliance violations or customer harm. This is why banking innovation leaders remain cautious. Hallucinations, bias, and unpredictable behaviour remain among the biggest blockers to deploying non-deterministic AI agents in production. In the context of financial advisory, these are red flags. A deterministic approach mitigates them by design: the agent cannot step outside its scripted bounds, and any data or content it presents is drawn from verified sources (e.g., the bank’s product database or official market data feeds, not a random internet search).

Another area is portfolio management and investment advisory. A deterministic AI workflow can assist in portfolio rebalancing by following the agreed strategy to the letter. For example, if a client’s portfolio drifts from a 60/40 stock-bond ratio, the AI suggests trades to restore balance, based on pre-set rules and the client’s risk profile. The suggestions will be consistent with the investment policy statement every time. A non-deterministic agent might try to “optimise” further and, in doing so, take actions that conflict with the client’s stated preferences or regulatory constraints (like short-selling a stock which might be against a policy). Consistency here is key, not just for compliance but for maintaining the client’s trust in the advice they receive. Clients of financial institutions need to know that the advice is not randomly changing with each conversation with an AI assistant – it should be steady, reliable, and backed by the institution’s proven expertise. Deterministic AI ensures that level of reliability, effectively encoding the institution’s best practices and compliance standards into each interaction.

Other Benefits: Efficiency, Fewer Errors, and Regulatory Alignment

Beyond the specific use cases, deterministic agentic workflows bring tangible, measurable benefits to banking operations:

• Faster Processing and Higher Throughput: By automating complex processes end-to-end, deterministic AI systems drastically cut down processing times. For example, as noted, credit papers that once took analysts days to research and write can be generated in seconds with AI. Loan approvals that took weeks are done in minutes. This speed not only improves customer satisfaction through faster responses but also enables banks to handle greater volumes without adding headcount.
• Reduced Error Rates: Manual processes are prone to human error: data entry mistakes, overlooked compliance checks, inconsistent risk assessments. Deterministic workflows perform the same tasks consistently and can be tested thoroughly, leading to fewer errors in output. Automated data validation and cross-checking can be built in. For instance, an AI that spreads financial statements (i.e., converts financial reports into structured data for analysis) can apply the same accounting rules uniformly, ensuring no calculation is missed. By pulling data directly and computing automatically, it avoids the transcription errors that often occur when analysts manually input figures. As a result, the financial analysis is more accurate and reliable. Overall, banks can expect lower operational losses and incidents thanks to this improved accuracy.
• Improved Compliance and Audit Readiness: As emphasised, compliance is ingrained in deterministic workflows. They continuously monitor and enforce rules, for example, flagging any loan that doesn’t meet credit policy, or ensuring every transaction is screened against sanction lists. This real-time compliance monitoring keeps the institution aligned with regulations at all times. Moreover, when regulators request reports or audits, the bank can quickly provide evidence of compliance. It’s no longer an onerous project to gather who did what and when – the system already has that record. This readiness can directly save costs by avoiding fines and reducing the effort spent on regulatory reporting. In one survey, risk leaders acknowledged that moving to automated, trackable AI processes in risk and compliance could strengthen controls and transparency across all lines of defence.
• Higher Productivity and Employee Focus: Deterministic AI agents take over the mundane, repeatable tasks, freeing up human employees to focus on higher-value activities. A loan officer can spend more time on complex cases or relationship-building, rather than number-crunching basic applications. A compliance officer can concentrate on interpreting new regulations rather than manually compiling data for reports. This shift not only boosts productivity but also morale, employees are relieved from drudgery and can contribute where their human judgement is truly needed. According to industry insights, such workflows can help alleviate the strain of staff shortages and reduce productivity losses from manual task switching​. In sum, the bank gets more done with the same or fewer resources.
• Scalability and Future-Proofing: Once a deterministic workflow is in place, scaling it is straightforward. Need to handle 2x the loan applications? The same workflow can be run in parallel or with more computing power. Because the process is well-defined, it’s easier to maintain and update as regulations or business needs change. This flexibility was highlighted by Juris AICraft’s design: banks can customise and extend workflows to fit their unique needs, ensuring that the system grows with them​. It provides a level of control and adaptability that banks require for long-term technology investments.

Conclusion: Aligning AI with Banking’s Core Priorities

In banking, consistency, trust, and compliance are non-negotiable. Deterministic agentic AI workflows directly align with these priorities, delivering results that are repeatable, explainable, and compliant. They complement the banking industry’s focus on risk management and accountability while harnessing automation and intelligent decision-making. In contrast, non-deterministic AI agents introduce uncertainty that outweighs their theoretical benefits, especially in highly regulated operations like credit underwriting, ESG compliance, and financial advisory.

Deterministic workflows empower banks to innovate responsibly, blending cutting-edge automation with the governance and predictability of traditional rule-based systems. For senior banking executives and innovation leaders, the message is clear: AI’s potential in finance is best unlocked through solutions offering consistent outcomes, robust audit trails, and regulatory alignment. Adopting deterministic agentic workflows is a proven path to sustainable and scalable AI deployment in financial services. It’s no surprise that Gartner’s recent banking technology trends and Forrester’s AI governance reports both allude to the need for controlled, compliant AI solutions in finance; the industry is moving in this direction out of necessity.

JurisTech, Your Preferred Partner

JurisTech understands the critical demands of banking. With Juris AICraft, our deterministic AI platform, financial institutions gain a predictable, transparent AI workforce—much like a seasoned team rigorously following the bank’s policies.

Juris AICraft enables banks to achieve:

• Faster, more consistent credit decisions
• Enhanced regulatory compliance and audit readiness
• Greater operational efficiency and accuracy
• Improved customer experiences built on trust

As regulatory scrutiny intensifies and customer expectations rise, Juris AICraft offers your institution the reliability and transparency necessary for sustained success. Don’t leave critical decisions to chance, partner with JurisTech, where AI innovation meets banking compliance.

Contact us for a free demo and explore how Juris AICraft can transform your banking operations today.

About JurisTech

JurisTech is a cloud-native, global-leading company specialising in enterprise-class lending and recovery software solutions for banks, financial institutions, telecommunications, and automobile companies worldwide. We embrace a microservices architecture to ensure scalability and flexibility in our solutions.

We power economies by reimagining financial services with cutting-edge software solutions, leveraging composable architecture and generative AI. Our offerings include artificial intelligence (AI), auto-decisioning, digital customer onboarding, loan origination, credit scoring, loan documentation, litigation, and debt collection. Our solutions have enabled businesses across a broad array of industries to undergo digital transformation, providing enhanced customer experiences and, most importantly, achieving their business goals.

JurisTech has been mentioned as a Representative Provider for Lending Ecosystems, as a Representative Vendor for Commercial Loan Origination Solutions, and as a Sample Vendor for Commercial Banking Onboarding across Gartner reports in 2024.