Exploring the Next Wave of AI: From Intelligence to Agency in Financial Services

Artificial Intelligence (AI) in financial services has already completed its first phase: predictive analytics, automated underwriting, chatbots, and anomaly detection are now commonplace. The next stage is more significant. AI agents will not only analyze and inform; they will negotiate, recommend, and take actions—within guardrails. These systems move beyond passive intelligence to become active counterparts in lending, investing, fraud response, and claims resolution.

This paper examines the rise of autonomous financial agents that combine machine learning, large language models (LLMs), reinforcement learning (RL), and secure execution frameworks. Using industry examples and pilots, we show how agents shift value creation while raising new requirements for governance, compliance, and human oversight.

1. The Evolution of AI in Finance: From Decision Support to Decision Execution

•  1980s–1990s: Expert systems for credit scoring and actuarial tables.
•  2000s–2010s: Predictive models in underwriting, algorithmic trading, and fraud detection.
•  2020s: Conversational AI, digital assistants, robo-advisors, and real-time transaction monitoring.

As of 2025, a new phase is underway. AI systems not only inform human decisions but increasingly act as autonomous agents able to:

•  Negotiate repayment schedules with borrowers.
•  Recommend and execute micro-investments in real time.
•  Detect fraud and independently hold, step-up authentication, or decline transactions.
•  Process simple claims end-to-end without human handling.

This shift is propelled by progress in multi-agent reinforcement learning (MARL), LLMs, and autonomous workflow orchestration.

2. Technical Foundations of Autonomous Agents in Finance

2.1 Multi-Agent Systems & Reinforcement Learning

Agents operate in environments where rewards map to financial KPIs (credit risk reduction, fraud loss prevention, yield optimization). Through RL, agents learn strategies that balance efficiency, compliance, and customer experience.

Example: A lending agent proposes repayment terms, simulates borrower responses, and adapts offers from historical outcomes and policy limits.

2.2 Language Models as Negotiators

Negotiation in finance is language-heavy. LLMs, grounded by Retrieval-Augmented Generation (RAG) over policy, product, and regulation libraries, draft compliant terms

and explanations. Instruction-following and tool calling ensure agents reference current rules before suggesting actions.

2.3 Secure Execution Frameworks

Autonomous agents must safely take action, not just decide:

•  API orchestration with core banking, trading, and claims platforms.
•  Optional smart contracts for automated settlement.
•  Authority limits, dual control, and human checkpoints for material or sensitive actions.
•  Immutable audit logs for Model Risk Management (MRM) and regulators.

3. Industry Applications

3.1 AI Agents as Negotiators

What it is: Autonomous systems that bargain and reach agreements with humans or other agents under goal, policy, and time constraints.

Why it matters: Negotiation is central to lending (rates, collateral), investments (allocations, fees), fraud & disputes (chargebacks, merchant liability), and insurance claims (payout amount and timing).

Core mechanisms:

•  Preference modeling: Utility functions encode objectives (e.g., affordability vs. risk).
•  Strategy formulation: Conceder, Boulware, tit-for-tat, or hybrid RL-optimized tactics.
•  Protocols & communication: Alternating-offers, auctions, Contract Net; natural language for human-agent dialogue; standardized APIs for machine-to-machine.
•  Learning & adaptation: RL and opponent modeling adjust to market shifts and counterparty behavior.

Illustrative domains:

•  Lending: Borrower and lender agents co-create terms (rate/tenor/collateral) within policy envelopes.
•  Investments: Portfolio agents negotiate fees or block-trade pricing.
•  Fraud & disputes: Agents converge on fair liability splits more quickly than manual arbitration.
•  Insurance claims: Claimant and insurer agents negotiate repair costs and timelines.

Mini Walkthrough — Home Loan Negotiation Borrower (Priya) and Bank agents model preferences (affordability vs. risk/return). Through alternating offers, the parties settle on 7.2% fixed, 18-year tenor, 1.5% prepayment penalty, house as collateral, 0.75% fee—executed via smart-contract rails. The system logs explanations, policies cited, and model versions.

Governance: Negotiation agents operate under bounded concessions, with escalation for exceptions and recorded reason codes for adverse actions.

3.2 AI Agents as Fraud Prevention Agents

What they are: Not a single model but an ensemble-driven system that can sense, reason, act, explain, learn, and collaborate—in near real time.

Agent capabilities:

•  Sensing: Logins, devices, KYC, transfers, card swipes, claims.
•  Reasoning: Supervised models + unsupervised anomalies + graph risk; retrieves case context.
•  Acting: Follow policy playbooks—allow, step-up, hold, decline, queue.
•  Explaining & learning: SHAP attributions, analyst feedback loops, drift checks.
•  Collaborating: With payments, AML, disputes, and customer-comms agents—privacy-preserving by design.

Problems where agents excel: Card & account fraud (ATO/CNP), real-time payments (APP scams), synthetic identities, merchant collusion, AML structuring/smurfing/layering, and friendly-fraud chargebacks.

Real-time ensemble (at transaction time ttt)

1. Feature build: velocity, behavioral, device/network, graph, merchant, and history features.

2. Parallel inference:

a. Supervised classifier → pSMp_{\mathrm{SM}}pSM

b. Anomaly model → scoreAMscore_{\mathrm{AM}}scoreAM

3. Calibration & fusion:

logitp=w1⋅logit(pSM)+w2⋅z(scoreAM)+w3⋅z(riskGR)+w4⋅rule_boost\text{logit}_p = w_1\cdot \text{logit}(p_{\mathrm{SM}}) + w_2\cdot z(score_{\mathrm{AM}}) + w_3\cdot z(risk_{\mathrm{GR}}) + w_4\cdot rule\_boostlogitp =w1 ⋅logit(pSM )+w2 ⋅z(scoreAM )+w3 ⋅z(riskGR )+w4 ⋅rule_boost pfraud=σ(logitp)(σ is the logistic function)p_{\mathrm{fraud}} = \sigma(\text{logit}_p) \qquad (\sigma \ \text{is the logistic function})pfraud =σ(logitp )(σ is the logistic function)

4. Cost-aware action: choose allow / step-up / decline to minimize expected loss, factoring customer friction and churn.

5. Explainability: Local SHAP attributions, graph snippets (“beneficiary 2 hops from mule cluster”), rule hits, versioned artifacts.

6. Resilience: PSI/KS drift tests, shadow/canary models, red-teaming, and a graceful kill-switch to conservative rules if anomalies spike.

Terminology: Use “deny lists” or “blocklists” (not “denylists”).

3.3 AI Agents in Claims Processing

Claims (insurance, card disputes, loan defaults, fraud reimbursements) are costly and sensitive. Agents cut cycle time and improve fairness by handling assessment, negotiation, fraud checks, settlement, and communications—with audit trails.

Why it matters (industry-reported): Claims operations can represent a large share of insurer expense; fraud estimates in the 10–15% range are often cited; delays drive dissatisfaction. Treat ranges as illustrative without formal citation.

What a claims agent does:

•  Intake & pre-processing: OCR/NLP on forms; computer vision on images; entity resolution.
•  Coverage validation: Retrieve policy terms; reason over eligibility and exclusions.
•  Fraud/risk scoring: Cross-check networks and prior claims; graph ML for link analysis.
•  Assessment & valuation: Benchmark costs; propose fair ranges.
•  Negotiation & communication: Counteroffers with clear explanations; merchant/partner outreach.
•  Decision & execution: Approve/partial/deny; trigger payouts; generate compliance reports.
•  Post-settlement learning: Update fraud signals, valuation models, and satisfaction metrics.

Walkthrough — Minor Auto Claim AI parses documents and images, validates coverage (₹10,000 deductible), benchmarks repairs, negotiates with the shop, and settles at ₹1,35,000 with instant payout and a full reasoning log. The repair shop is flagged for inflated quotes; models update accordingly.

3.4 AI Agents in Lending

Lending is moving from static scorecards to agentic systems that engage across the credit lifecycle—negotiating, suggesting, and executing within policy and oversight.

Why now:

•  Market: Tight margins, volatile losses, and customer demand for fast, flexible decisions.
•  Technology: LLMs for compliant drafting; constrained optimization and RL for pricing/limits; secure APIs and guardrails for reversible actions.
•  Regulation: Specific adverse-action reasons are required by the CFPB under ECOA/Reg B; supervisors expect robust MRM per FRB SR 11-7/OCC guidance. The EU AI Act treats credit decisioning as high-risk, requiring documentation, logging, robustness, and security.

Agent personas (policy-bounded with escalation):

•  Prospecting & Pre-Qualification Agent – screens eligibility, proposes conditional APR/limit, explains via RAG, pre-fills applications with consent.
•  Underwriting & Pricing Agent – hybrid PD/LGD/EAD models; constrained optimization; provides policy-aligned reasons and adverse-action notices.
•  Documentation & Closing Agent – drafts disclosures with retrieval over policy libraries; validates clauses; coordinates e-signatures and audit logs.
•  Servicing & Customer Care Agent – retrieves balances and payoffs; executes small changes (due-date shifts, autopay) within allow-lists.
•  Collections & Hardship Agent – RL-based payment plans, deferrals, settlements by micro-segment; policy-checked communications.
•  Portfolio Monitoring & Early-Warning Agent – detects pre-delinquency signals; initiates proactive interventions.

How it works (technical):

•  Reasoning & dialogue: Finance-tuned LLMs with RAG; tool calling separates language from actions.
•  Decisioning & optimization: Gradient boosting/deep tabular models for PD; constrained pricing for utility under caps and fairness; counterfactual explainers for borrower feedback.
• Safe RL/bandits: Bounded action spaces; off-policy evaluation; A/B shadowing; tiered authority.
•  Execution: Action gateway enforces allow-lists, dual control, compensating transactions, PII minimization, and red-teaming against prompt injection.

Industry examples (reported):

•  Zest AI with credit unions/banks – reported approval lifts while controlling risk.
•  OakNorth – forward-looking SMB credit monitoring and scenario analysis.
•  TrueAccord – machine-learning-driven digital collections with engagement/recovery improvements.
•  Upstart – emphasizes transparency and fairness monitoring in AI underwriting.

4. End-to-End Example: Agentic AI Across the Lending Lifecycle

1) Foundations (pre-launch) Load approved policies (credit box, pricing caps, fees, state variations) into a policy library; index regulations for retrieval. Register allowed tools (KYC, bureau pull, pricing calculator, Loan Origination System (LOS) actions). Set authority limits, escalation thresholds, and a decision diary (prompts, data, rules, actions). Controls: MRM validation, drift/fairness monitors, prompt/tool-use guardrails, reversible actions. KPIs: Policy coverage, test pass rate, % reversible actions, mean time to detect drift.

2) Discovery & Pre-Qualification Estimate eligibility and conditional APR/limit; conversationally collect missing data; pre-fill forms; schedule ID&V with consent. KPIs: Qualified lead rate, abandonment, time to pre-qual, complaint rate.

3) Application Intake OCR documents; flag inconsistencies; propose minimal additional evidence; route edge cases. KPIs: Completion rate, re-request rate, handle time.

4) KYC/AML & Fraud Pre-Screen Sanctions/PEP lists, device fingerprints, consortium fraud signals; recommend step-up verification as needed. KPIs: False-positive rate, verification pass rate, time to clear.

5) Underwriting & Limit/Term Construction Approve/decline/verify; initial APR/limit/tenor via constraints (APR caps, DTI, portfolio limits). KPIs: Approval lift at constant loss, Gini/KS, escalations, adverse-action accuracy.

6) Pricing & Terms Dialogue Offer 2–3 policy-safe bundles (payment comfort vs. tenor); bounded concessions with logged policy citations. KPIs: Offer acceptance, re-quote rate, quote NPS, margin delta.

7) Documentation & Closing Generate contract pack; validate clauses; collect e-sign; book loan; update core. KPIs: Document error rate, time to sign, post-offer fallout.

8) Funding & Disbursement Select rails; release funds; start repayment; autopay setup. KPIs: Time to cash, failed disbursements, funding exceptions.

9) Onboarding & Early Servicing Align reminders to salary credits; allow due-date shifts within rules; deflect routine calls. KPIs: Autopay adoption, first-bill success, call deflection.

10) Early-Warning & Proactive Intervention Spot income volatility or utilization spikes; offer micro-adjustments; flag systemic patterns. KPIs: Roll-rate reduction, right-party contact, pre-delinquency cure.

11) Collections & Hardship Management Micro-segmented best-next actions; empathetic scripts; escalate beyond limits. KPIs: Cure rate, net recoveries, complaints/NPS, legal referrals avoided.

12) Extensions, Top-ups & Cross-Sell Pre-approved top-ups or refinancing; affordability re-checks; cooling-off periods. KPIs: Repeat booking rate, CLV (Customer Lifetime Value) uplift, churn reduction.

13) Portfolio Feedback & Policy Tuning Vintage curves, cohort losses, fairness and complaint themes; propose policy shifts within bands; quarterly re-validation. KPIs: Approval-at-loss frontier, loss volatility, fairness parity, audit findings.

5. Oversight, Governance & Compliance

•  Explainability: Per-decision reason codes, SHAP attributions, and counterfactuals (“If device had 3 prior good transactions, decision would be allow”).
•  Validation & monitoring: Champion/challenger, drift tests (PSI/KS), bias checks; calibration stability by segment.
•  Security & privacy: PII minimization, tokenization, encryption, secret management, red-team exercises; safe LLM patterns with retrieval-only answers and strict tool boundaries.
•  Auditability: Versioned models, prompts, retrieved policies, inputs/outputs, and actions—immutable logs.
•  Regulatory posture: Align with CFPB adverse-action expectations (ECOA/Reg B), FRB SR 11-7/OCC MRM guidance, and EU AI Act obligations for high-risk systems.

Future Outlook

• AI agents in finance are progressing toward multi-agent systems where:
•  Banks, investors, regulators, and customers use agents that negotiate in real-time.
•  Markets shift into continuous, AI-mediated bargaining environments.
•  Smart contracts on blockchain execute automatically after agents agree.
•  Regulators employ supervisory AI agents to ensure fairness and compliance.

Conclusion: Agents as the Future Counterparties

The financial system is moving from AI as an advisor to AI as a counterparty. In lending, investing, fraud prevention, and claims, agents will negotiate terms, execute trades, and finalize settlements. This change is not about replacing humans but about scaling financial intelligence into millions of small decisions each day.

Institutions that embrace this responsibly by including transparency, oversight, and governance will gain efficiency and reshape customer trust and engagement in the digital age.