Introduction
AI is no longer an experiment for banks, insurers, and asset managers — in 2025 it’s a set of practical tools that cut costs, speed decisions, and reduce risk. This article walks through the AI applications that reliably move the needle today: where organizations are getting measurable wins, what to prioritize first, and how to govern these systems so regulators and customers stay calm.
You’ll see clear examples — fraud and AML detection that work in near real time, credit and underwriting models that use alternative data while remaining explainable, advisor co‑pilots that free up human time, and compliance automation that scales across jurisdictions. Along the way we’ll highlight playbooks you can ship quickly and the controls you need to keep operations safe and auditable.
Regulatory & compliance assistants can process updates 15–30× faster across dozens of jurisdictions, reduce documentation errors by ~89%, and cut regulatory filing workload by 50–70% — enabling major reductions in manual effort and audit risk.
Read on for the short list of high‑value use cases, sector‑specific snapshots for banking, insurance, and investment services, and a practical 90‑day roadmap that turns a pilot into production without getting lost in tech experiments. If you want AI that actually delivers in finance, this is the guide that skips theory and focuses on what you can ship and measure.
The short list: where AI delivers outsized value in finance
Fraud, AML, and anomaly detection: real‑time patterns and network analysis to cut losses
AI excels where data velocity and complexity overwhelm human teams. Streaming transaction scoring, graph‑based link analysis and behavior clustering detect money‑laundering rings, bot farms and payment fraud in near real time — cutting dwell time and financial loss. Firms combine supervised models for known fraud patterns with unsupervised anomaly detectors (and rapid feedback loops) to reduce false positives while surfacing emerging attack types. The highest returns come from integrating detection with orchestration: automated case enrichment, evidence collection and prioritized investigator queues that turn alerts into recoveries faster.
Credit scoring and risk underwriting: alternative data with explainability that passes audits
Lenders and underwriters are using AI to expand coverage and improve risk precision. Models that ingest alternative signals — transaction flows, utility and rent payments, device and behavioral signals — unlock credit for underserved segments while improving portfolio risk segmentation. Crucially for regulated use cases, teams pair complex models with explainability layers, counterfactual checks and scorecards so decisions are auditable and remediations are straightforward. This combo preserves performance gains without sacrificing compliance or auditability.
Customer engagement and service: chat, voice, and agent assist to lift CSAT and slash wait times
Generative AI and real‑time speech analytics transform client interactions. Virtual assistants deflect routine queries, synthesize account context for agents, and automate follow‑ups so customers get answers faster and agents spend more time on high‑value work. Proven outcomes include materially higher CSAT and faster resolution: AI‑assisted contact centers raise first‑contact resolution, cut average handle times and enable targeted upsell at scale — all while keeping conversation logs and compliance checks embedded in the workflow.
Portfolio management and advisory co‑pilots: planning, reporting, rebalancing under fee pressure
Asset managers and wealth teams face fee compression and scale pressures; AI co‑pilots address both. Advisor assistants automate reporting, generate client narratives, surface rebalancing opportunities and run scenario planning — saving advisors hours per week and lowering cost per account. Where deployed well, these tools act as productivity multipliers: they let advisors focus on advice and relationships while routine analysis, compliance checks and client communications are automated and documented.
Document and compliance automation: KYC/Onboarding, reporting, reconciliations at scale
Back‑office and regulatory workflows are low‑risk, high‑value targets for AI. Automated document ingestion, entity resolution, rules engines and template generation speed onboarding, reconciliations and filing preparation while reducing manual error. In practice this shows up as dramatic efficiency gains and lower audit risk: “15-30x faster regulatory updates processing across dozens of jurisdictions (Anmol Sahai). 89% reduction in documentation errors (Anmol Sahai). 50-70% reduction in workload for regulatory filings (Anmol Sahai).” Insurance Industry Challenges & AI-Powered Solutions — D-LAB research
These five plays — fast detection, explainable credit, conversational CX, advisor co‑pilots and compliance automation — represent the highest‑ROI entry points for most financial institutions. In the next section we’ll translate these plays into concrete, sector‑level examples so you can see how the same building blocks are applied differently by banks, insurers and investment managers.
Sector snapshots: banking, insurance, and investment services
Banking and payments: personalization, collections, surveillance, and model‑driven pricing
Banks are applying AI across the customer lifecycle: personalization engines tailor offers and pricing, real‑time surveillance flags suspicious activity, and predictive models improve collections by prioritizing interventions. The highest value comes from combining customer signals (transactions, product usage) with operational workflows so models trigger automated, auditable actions — for example dynamic outreach, prioritised investigator queues, or price adjustments — rather than just producing standalone scores.
Implementation notes: start with narrowly scoped pilots that tie model outputs to a single automated workflow, instrument feedback loops for continuous improvement, and embed explainability and governance so pricing and surveillance models remain auditable.
Insurance: underwriting assistance and touchless claims to fix cycle time and leakage
Insurers benefit when AI reduces manual review and speeds decisions. Underwriting assistants that summarize documents, highlight risk drivers and suggest pricing inputs help underwriters process more cases with consistent quality. On the claims side, automated intake, image analysis and rule‑based adjudication enable “touchless” settlements for straightforward claims while routing complex cases to specialists. Together these approaches shrink cycle times and reduce leakage from delays and inconsistencies.
Operational guidance: prioritize data quality for imagery and policy documents, instrument clear escalation gates for exceptions, and align automation with existing controls so claims automation improves customer experience without increasing financial or regulatory risk.
Investment services and wealth: advisor co‑pilot, financial planning, client outreach, compliant comms
In investment and wealth management, AI acts as a force multiplier for advisors. Co‑pilots generate client narratives, automate reporting and run scenario analyses; client assistants deliver personalized planning and timely outreach; and supervised generation ensures communications remain compliant. The combination lowers per‑account servicing costs while freeing advisors to focus on strategy and relationships.
Deployment tips: integrate the co‑pilot close to advisors’ workflows (CRM, portfolio systems, reporting tools), maintain human‑in‑the‑loop review for client‑facing outputs, and enforce content controls to prevent non‑compliant language or risky recommendations.
Across sectors the common theme is not a single breakthrough model but pragmatic automation: start small, connect AI outputs to actions, monitor outcomes, and build governance into every workflow. In the next part we’ll convert these sector priorities into concrete, repeatable playbooks and quick‑win implementations you can deploy rapidly.
Proven AI playbooks you can ship this quarter
Advisor Co‑Pilot (wealth/asset management)
What to build: a workflow‑embedded assistant that auto‑generates client reports, synthesizes portfolio insights, surfaces rebalancing suggestions and drafts compliant client communications. Integrate with CRM, portfolio accounting and document stores so the co‑pilot has current positions, mandates and recent conversations.
Quick steps to ship this quarter: (1) pick a 50–200 account pilot where advisors agree to co‑pilot drafts; (2) map required data feeds (holdings, transactions, CRM notes, client profiles); (3) deploy a guarded LLM with retrieval‑augmented generation and template controls; (4) enable human review and capture feedback for model retraining; (5) measure advisor hours saved and cost per account.
Expected impact and tools: “AI advisor co-pilots have delivered outcomes like a 50% reduction in cost per account, 10–15 hours saved per advisor per week, and up to a 90% boost in information processing efficiency — driving immediate operational savings and scalability.” Investment Services Industry Challenges & AI-Powered Solutions — D-LAB research. Common vendors and components: Additiv, eFront, BuddyX by Fincite, DeepSeek R1.
AI Financial Coach / Investor Assistant
What to build: a client‑facing coach that answers basic planning questions, runs simple simulations, nudges clients with personalized education and triages complex queries to advisors. Tie the assistant to secure account data and pre‑approved advice templates so outputs remain compliant.
Quick steps to ship this quarter: deploy a lightweight web/chatbot front end connected to a knowledge base of product rules and FAQs; instrument session logging and consent; run a soft launch with a subset of users for product tuning.
Expected impact and tools: improved engagement and faster support resolution — common deployments report uplift in client engagement and reduced call wait times. Tools and partners for rapid rollout include Wipro, IntellectAI and Unblu.
Underwriting Virtual Assistant
What to build: an underwriter helper that ingests applications, medical/inspection reports and external data, then summarizes key risk drivers, proposes pricing inputs and highlights exceptions requiring manual review. The assistant should output a concise risk brief plus a recommended decision and rationale.
Quick steps to ship this quarter: connect intake documents to an OCR+NLP pipeline, create standardized underwriting templates, set exception thresholds for human review, and train the model on historical decisions to surface likely flags.
Expected impact and tools: material productivity gains for underwriting teams and more consistent pricing. Common enterprise tools/vendors in production deployments include Cognizant, Shift Technology and Duck Creek.
Claims Processing Assistant
What to build: an automated claims intake and triage flow that classifies claim types, extracts evidence from photos/documents, runs fraud detection checks and either pays simple claims automatically or routes complex claims to specialists with a pre‑filled investigation bundle.
Quick steps to ship this quarter: build an API chain for image analysis + document extraction, integrate rule engines for touchless eligibility, instrument a human escalation path and measure cycle time reduction from end‑to‑end.
Expected impact and tools: faster cycle times and lower leakage from fraud and manual error. Vendors commonly used for pilots and scale include Lemonade (tech patterns), Ema and Scale.
Regulation & Compliance Tracking Assistant
What to build: a monitoring and synthesis system that ingests regulatory updates, maintains a rules catalogue, maps changes to affected processes and drafts filing templates or task lists for compliance teams. Supply a searchable audit trail and automated evidence collection for internal and external audits.
Quick steps to ship this quarter: deploy connectors to regulatory feeds and policy repos, create a change‑impact classifier, automate drafting of standard responses and route high‑risk items to legal for review.
Expected impact and tools: large reductions in manual review time and filing workload; common vendor choices include Compliance.ai, Canarie AI and RCG Global Services. Read more on regulatory technology compliance.
How to measure success quickly: pick 2–3 KPIs per playbook (hours saved, cycle time, % touchless transactions, fraud loss rate, cost per account) and instrument baseline metrics before the pilot. Keep the initial scope narrow, require human sign‑off for customer‑facing outputs, and iterate with weekly feedback loops.
These playbooks are designed for rapid implementation: narrow scope, one or two data feeds, human‑in‑the‑loop controls and clear KPIs. Once a pilot proves value, scale by expanding cohorts, hardening governance and adding automation for routine exceptions — and then put in the guardrails and monitoring needed to operate at enterprise scale.
Thank you for reading Diligize’s blog!
Are you looking for strategic advise?
Subscribe to our newsletter!
Risk, governance, and security that regulators will accept
Model governance: monitoring, bias checks, explainability for credit/underwriting and trading
Start with an explicit model inventory and lifecycle policy: catalogue models, owners, intended use, data sources and approval status. Require pre‑deployment validation (performance, stress tests, stability) and post‑deployment monitoring for drift, data quality and population shifts. Embed regular bias and fairness checks (group metrics, disparate impact testing) and maintain human review gates for high‑risk decisions.
Operationalize explainability: produce both global model documentation (design decisions, training data summaries, limitations) and local explanations for individual decisions that touch customers or capital. Ensure explainability outputs are consumable by compliance teams and can be translated into remediation steps for front‑line staff and auditors.
Data controls: PII minimization, lineage, retrieval‑augmented generation to curb hallucinations
Treat data as the control plane. Enforce least‑privilege access, strong encryption in transit and at rest, and automated data classification so PII is discovered and handled consistently. Apply pseudonymization or tokenization for datasets used in model training and testing to reduce exposure.
Implement lineage and cataloging so every model prediction can be traced back to the dataset, transformation steps and model version. For systems using retrieval‑augmented generation, lock down retrieval endpoints, sanitize source documents, and maintain provenance metadata so generated outputs can be audited and sources reproduced. Build automated checks for hallucinations and confidence scoring and route low‑confidence outputs to human review.
Cybersecurity frameworks to protect IP and client data: ISO 27002, SOC 2, NIST 2.0
Adopt an accepted security baseline and map controls to it (for example, ISO 27002, SOC 2, or NIST guidance) to align internal practice with regulator expectations. Key controls include identity and access management, multi‑factor authentication, strong key and secrets management, network segmentation between model development and production environments, and endpoint detection and response.
Extend controls to the ML supply chain: verify third‑party model and data vendors, require secure development practices, sign SLAs for incident response, and test backups and disaster recovery. Incorporate continuous vulnerability scanning and periodic red‑teaming of model endpoints and APIs to detect abuse vectors and data exfiltration risks.
Regulatory automation: AML/KYC evidence, audit trails, and controls embedded in workflows
Design AI systems so compliance is a byproduct of the workflow. Capture structured evidence with every automated decision (input snapshot, model version, score, explanation, approver ID, timestamps) and store it in an immutable, searchable audit trail. Integrate evidence capture with case management so investigators can retrieve the full decision context quickly.
Automate rule mapping and impact analysis: when a regulatory change occurs, systems should flag affected rules, surface impacted processes and generate task lists for remediation. For AML/KYC, combine model outputs with human annotations to create defensible, annotated records that satisfy auditors and can be used to improve models over time.
Practical checklist to start: maintain a model inventory with owners; require an approval workflow for any model touching customers or capital; instrument continuous monitoring and alerting for drift and performance; enforce strict data governance and lineage; apply security controls across cloud and on‑prem environments; and capture auditable evidence for every automated action. These controls reduce regulatory friction and make it feasible to scale AI safely.
With governance and security scaffolding in place, the natural next step is a compressed implementation plan: how to pick the first use cases, assemble data and tech, and run a fast, measurable pilot — a practical 90‑day playbook you can follow to move from policy to production.
A 90‑day roadmap to implement AI applications in financial services
Prioritize 1–2 use cases tied to fee compression or talent gaps; write the measurable business case
Week 0–1: executive alignment and selection. Convene a short steering group (product, ops, legal, security, an end‑user champion). Screen candidate use cases against three filters: commercial impact (cost reduction or revenue protection), data readiness, and regulatory risk. Choose 1–2 pilots with clear owners.
Week 2: build the business case. For each pilot produce a one‑page case that includes: problem statement, target KPI(s) and baseline, expected delta and payback, required people and systems, and success criteria for go/no‑go at 90 days. Secure a small dedicated budget and a working sponsor.
Data and integration checklist: CRM, call logs, policy docs; lakehouse, event streams, secure connectors
Week 1–3: data discovery and quick wins. Inventory required sources, owners and refresh cadence. Prioritize the minimal feeds to unlock the pilot (for example: customer master + transaction history, or policy documents + claims images).
Week 3–6: secure ingestion and staging. Set up a sandboxed data plane (lakehouse or secured bucket) with automated connectors, schema documentation and retention rules. Apply PII discovery and masking on any training or development datasets and record lineage for every table.
Deliverable at day 45: a reproducible data snapshot and an agreed integration plan for production delivery (connectors, streaming vs batch, SLA).
KPIs and target ranges: cost per account, claim cycle time, CSAT, fraud loss rate, advisor hours saved
Day 0–7: define 2–3 primary KPIs per pilot and one leading indicator. For each KPI set a baseline and define an achievable target range for 30/60/90 days. Examples: percent of claims processed touchlessly, mean time to decision, advisor hours per client per week, false positive rate for alerts.
Day 7–30: instrument measurement. Implement automated dashboards that report baseline and live progress, include cohort breakdowns and an error/exception log so teams can quickly diagnose regressions. Use weekly checkpoints to validate assumptions and surface blockers.
Buy vs. build: vendor shortlists, LLM choice, orchestration, MLOps, monitoring, red‑teaming and rollback
Week 2–5: rapid vendor evaluation. For constrained pilots prefer composable vendors or managed platforms that provide pre‑built connectors, explainability tooling and compliance controls. Evaluate vendors on integration effort, security posture, support model, upgrade/rollback procedures and total cost of ownership.
Week 4–8: select model and orchestration. If using LLMs, choose a provider or hosted model that supports fine‑tuning or retrieval augmentation and meets data residency/compliance needs. Architect an orchestration layer that separates prompt/template logic from the model so you can swap models with minimal code change.
Week 6–12: production hardening. Implement MLOps basics—versioned training data, model versioning, automated CI for pipelines, and continuous monitoring for data and concept drift. Run adversarial tests and a short red‑teaming exercise for client‑facing artifacts; establish rollback plans that switch to safe, deterministic responses or human‑only workflows on anomaly detection.
Governance, security and change management run in parallel: require legal review of customer‑facing content, maintain an immutable audit trail for decisions, and train impacted teams on new workflows before go‑live. At 90 days you should have a validated MVP, measured KPI deltas, documented runbooks, and a scaling plan (roles, tech investments and an estimated roadmap for months 4–12).
With those artifacts in hand you can decide whether to scale the pilot, add automation for exceptions, or take a different use case forward — all while retaining the controls and metrics that make the program auditable and repeatable.