How AI Is Helping Financial Services Companies in Chicago Cut Costs and Improve Efficiency

Chicago and Illinois matter for AI in financial services because the region pairs deep research capacity - Argonne National Laboratory's AI and exascale computing capabilities and university consortia like Northwestern and UChicago - with a $57.4 billion Chicagoland AI economy and over 164,000 AI jobs, creating fertile ground for banks, insurers and fintechs to deploy cost-cutting automation, fraud detection and faster model training at scale; see the World Business Chicago analysis and Argonne's AI program for how research, supercomputing and industry partnerships converge to accelerate trustworthy AI, while practical workforce options such as Nucamp AI Essentials for Work bootcamp registration help financial leaders translate those regional assets into operational savings.

For Chicago firms, the “so what” is concrete: local access to top-tier compute, talent pipelines, and applied research shortens pilot cycles and lowers vendor dependence.

World Business Chicago analysis of the Chicagoland AI economy and Argonne National Laboratory AI program overview detail these strengths.

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“Our lab's advanced computational capabilities are ideally suited for the challenges posed by the enormous datasets from astronomical surveys. By developing AI-driven solutions, we are accelerating discoveries that will reshape our understanding of the universe.”

Chicago firms turn AI into real-time competitive advantage by using machine learning to scan market activity, economic trends and risk signals so analysts spot opportunities and vulnerabilities that once took hours to surface; local research shows these methods can push markets toward “real-time efficient” prices when investors continuously learn from high‑dimensional data (Chicago Partners analysis of AI impact on financial services (2025), Chicago Booth research on AI and finance).

For trading desks and quant teams, that means faster hypothesis testing and fewer overnight model reruns; for wealth and portfolio teams, automated research tools summarize news, filings and sentiment into actionable signals.

The city's ecosystem - consultancies, AI dev shops, and infrastructure providers - supports this cycle end-to-end: infrastructure partners build ultra‑low latency, HPC pipelines so production analytics keep pace with markets, and some integration services claim they cut on‑site deployment time by up to 85%, shortening pilot-to-production timelines and lowering vendor dependence (AHEAD on AI and HPC for financial services).

“The capital markets sector faces unprecedented technology challenges as trading strategies become more data-intensive and markets operate at increasing speeds,” said Josh Perkins, Vice President of Emerging Technologies.

Great Lakes Credit Union's deployment of the conversational AI assistant Olive demonstrates how Illinois financial firms can cut contact-center costs while improving member access: launched in August 2023 in partnership with interface.ai, Olive now fully handles over 60% of inbound calls during business hours and more than 75% after hours, increasing fully serviced calls by over 200% versus the prior IVR and freeing staff to take higher‑value, consultative roles with better pay; see GLCU's rollout details and the broader interface.ai analysis of generative AI use cases in banking for operational context.

The bot's 24/7 transaction capabilities (balances, transfers, payments), Spanish language support added in 2024, and voice authentication pilots (hundreds of authentications) make the “so what” concrete: sustained containment gains reduce live‑agent hours and redirect labor to complex member needs, preserving service quality in Chicagoland credit unions facing tight margins.

Metric	Value
Olive launch	August 23, 2023
Inbound calls handled (business hours)	Over 60%
Inbound calls handled (after hours)	Over 75%
Increase in fully serviced calls	More than 200%
GLCU members	~95,000 (Chicagoland & surrounding areas)

“By using Olive to help with simpler requests, we're freeing up our employees to use their skills to assist with more complex transactions.” - Elizabeth Osborne, COO, Great Lakes Credit Union

Chicago and Illinois financial institutions are increasingly using AI to detect fraud, automate risk scoring and monitor transactional anomalies, but the Consumer Financial Protection Bureau's guidance makes clear that deploying black‑box models does not remove legal obligations: lenders must provide accurate, specific reasons when taking adverse actions such as credit denials, and cannot rely on generic checklists (CFPB guidance on credit denials by lenders using artificial intelligence).

The practical consequence for Chicago risk teams is concrete - fraud‑detection pipelines need integrated explainability reports, impact assessments and consumer‑facing explanations to reduce complaints and regulatory scrutiny - so pairing predictive models with documented disclosure and vendor due‑diligence workflows pays off operationally.

Firms expanding access with alternative scoring should also align models to fairness and disclosure practices highlighted in local guidance and industry playbooks (see how Zest AI credit scoring fairness framework frames fairness and compliance and Nucamp's Nucamp AI Essentials for Work practical compliance checklist for disclosures and impact assessments).

The “so what”: explainability becomes an operational control that directly guards margins by reducing dispute volumes and enforcement exposure.

“Technology marketed as artificial intelligence is expanding the data used for lending decisions, and also growing the list of potential reasons for why credit is denied,” said CFPB Director Rohit Chopra.

AI-driven underwriting in Illinois is already proving it can widen access without sacrificing compliance: models like Zest AI

open lending to thin-file customers while preserving fairness and regulatory compliance

, letting lenders consider applicants who previously lacked traditional credit histories (Zest AI credit scoring for thin-file borrowers).

Turning those model gains into durable cost and risk improvements requires governance - disclosures, impact assessments and vendor due diligence guided by a practical compliance checklist - so decisions remain explainable to regulators and consumers (AI compliance checklist for financial services in Chicago (2025 guide)).

Workforce transitions matter, too: employers such as Gallagher can support underwriting teams with targeted training and hiring pilots so staff shift into oversight and exception‑handling roles rather than routine manual reviews (Gallagher underwriting workforce transition and support programs).

The so what: expand credit access to previously underserved applicants while keeping regulatory exposure manageable.

Back-office automation is turning routine compliance chores into a competitive advantage for Chicago and Illinois financial firms: a local registered investment adviser automated alternative‑investment statement downloads to remove repetitive manual pulls and speed reconciliations (Chicago RIA robotic process automation case study), while enterprise tools - Microsoft Power Automate examples show everything from a 20 million‑record medical‑records pipeline that saved 100,000+ hours to Microsoft IT's revenue processing that runs on hundreds of millions of transactions and millions of automated instances - demonstrate scale and auditability for regulated workflows (Microsoft Power Automate real‑world automation stories).

Combined with ERP automation like Dynamics 365 Finance & Operations that enforces workflow rules for invoicing, SOX controls and real‑time reporting, Chicago teams can shorten audit windows, reduce reconciliation errors, and reassign staff to exceptions and regulatory attestations rather than data entry (Dynamics 365 Finance & Operations invoicing and SOX controls automation).

The so what: automated, auditable pipelines both cut operating cost and harden compliance posture, lowering dispute volumes and vendor risk during examinations.

“Using Power Automate RPA has been transformative for AT&T... Our employees continue to embrace and enable business transformation using Intelligent Automation.”

Chicago trading desks and portfolio teams are using AI not to replace judgment but to sharpen it: advanced models such as LSTM networks, Transformer architectures and reinforcement‑learning strategies have been shown to improve predictive accuracy in volatile markets, giving quants faster, more adaptive signals for positioning and risk‑parity rebalancing (SSRN paper on AI‑Driven Financial Forecasting); at the same time, leading economists caution the gains will be modest unless firms invest in domain‑specific data and worker‑augmenting workflows - Acemoglu estimates only a 1.1–1.6% GDP lift over a decade and urges a focus on complementary uses that boost analyst productivity (MIT analysis: Daron Acemoglu on the economics of AI).

The practical “so what” for Illinois: local asset managers can extract crisper short‑term signals and reduce manual research time by operationalizing tailored models, but must pair them with robust out‑of‑distribution stress tests and firm‑level data governance to avoid costly misfires when markets behave unlike training histories.

Evidence	Key point
SSRN forecasting paper	LSTM/Transformer/RL models outperform traditional methods in turbulent periods
Acemoglu (MIT)	AI likely yields modest productivity gains; complements not substitutes are key

“What are the apps that are really going to change how we do things?”

Quality control and digital verification in Illinois financial services are shifting from manual spot‑checks to continuous, AI‑assisted assurance: Great Lakes Credit Union's Olive proves the model - automating routine transactions while adding layered verification so exceptions go to trained staff instead of being buried in a queue - which sharpens accuracy, shortens resolution times and reduces dispute churn.

Olive's 24/7 transaction handling and voice authentication program (175 members enrolled, over 1,500 authentications performed) supply auditable trails that strengthen identity proofing and speed downstream reconciliations, and bilingual support (Spanish launched in 2024) closes access gaps for diverse Chicagoland members; see the credit union's Spanish rollout and the COO's congressional testimony for detailed metrics and operational context.

The practical payoff for Illinois firms is concrete: higher containment rates free experienced employees to focus on exception review and quality oversight, turning verification automation into a direct margin and compliance control rather than a back‑office risk.

Interface AI COO Congressional Testimony on AI in Financial Services with Olive Metrics | Great Lakes Credit Union: Olive Adds Spanish Support (August 2024)

Metric	Value
Olive launch	August 23, 2023
Inbound calls handled (business hours)	Over 60%
Inbound calls handled (after hours)	Over 75%
Increase in fully serviced calls	More than 200%
Voice authentication enrolled members	175
Voice authentications performed	Over 1,500
Spanish language support	Launched August 2024

“Olive consistently handles over 60% of total inbound calls during business hours and over 75% of all calls after business hours.”

Chicago firms can point to concrete, auditable gains: Great Lakes Credit Union's Olive virtual assistant now fully handles over 60% of inbound calls during business hours and more than 75% after hours, increasing fully serviced calls by over 200% since launch - containment that directly cuts live‑agent hours, shortens resolution times, and lets teams redeploy labor into higher‑value, consultative work that improves retention and reduces recruiting pressure (Great Lakes Credit Union Olive virtual assistant Congressional testimony).

When those operational savings are paired with practical vendor due‑diligence and disclosure playbooks, Illinois institutions can compress contact‑center costs without increasing regulatory risk; see a practical compliance checklist and deployment playbook for Chicago firms and examples of alternative scoring that preserve fairness and access (AI compliance checklist for Chicago financial services (2025), Zest AI alternative credit scoring examples for fairness and access).

The so‑what: measurable containment and servicing gains translate into fewer agent hours and clearer paths to redeploy payroll into oversight, analytics and member advising.

Metric	Value
Inbound calls handled (business hours)	Over 60%
Inbound calls handled (after hours)	Over 75%
Increase in fully serviced calls	More than 200%

“Olive consistently handles over 60% of total inbound calls during business hours and over 75% of all calls after business hours. Olive has increased the number of calls fully serviced by the virtual assistant by over 200% since launch.”

Operational resilience in Illinois financial firms now hinges on three linked pillars: governance, workforce, and resilient infrastructure - because even well‑intentioned automation magnifies exposure without them.

Recent Treasury guidance highlights immediate AI‑related operational risks from cybersecurity to fraud and flags a widening human‑capital gap that requires role‑specific AI training (U.S. Treasury report on AI-specific cybersecurity risks); Red Hat's operational‑resilience overview shows why technology choices matter, noting 84% of firms use public cloud, 60% reported a major incident in the prior year, and 43% now rely on a single vendor, which increases single‑point‑of‑failure risk unless firms adopt hybrid cloud, IaC and automation to preserve portability and failover (Red Hat FSI operational resiliency overview).

Governance readiness remains uneven - an ACA/NSCP benchmarking study found only 32% have an AI governance group and 12% adopted an AI risk framework - so the practical “so what” is stark: without documented vendor oversight, testing protocols and targeted reskilling, Chicago institutions risk longer outages, higher remediation costs and regulatory scrutiny; with them, incidents become recoverable events that free staff to focus on exceptions and higher‑value work (ACA/NSCP AI governance benchmarking 2024).

Metric	Source / Value
Financial firms using public cloud	84% - Red Hat
Firms reporting a major incident in past year	60% - Red Hat
Firms relying on a single cloud vendor	43% - Red Hat
Firms with AI governance group	32% - ACA/NSCP
Firms with AI risk management framework	12% - ACA/NSCP
Firms lacking third‑party AI policies	92% - ACA/NSCP

“There's widespread interest in AI, but a disconnect in establishing safeguards. Many firms recognize AI's potential but lack responsible management frameworks, exposing them to regulatory risk.”

Illinois financial leaders must treat AI not only as an efficiency tool but as regulated technology: a fast-moving mix of bipartisan federal bills, agency enforcement and technical guidance means lenders and fintechs in Chicago will face new disclosure, explainability and labeling obligations - including Illinois‑relevant measures like H.R.1027 (Disclosures for robocalls using AI), introduced by Rep.

Eric Sorensen (D‑IL) on 2/06/2025 - so call‑center and voice‑authentication pilots need compliance hooks now. Expect parallel pressure from regulators and standards bodies: the FTC already enforces deceptive AI claims (for example, a finalized DoNotPay order on 2/11/2025) while NIST's AI Risk Management Framework and federal hearings lay out expectations for impact assessments, transparency and vendor oversight.

Practical “so what”: a missed disclosure or weak vendor due‑diligence plan can produce enforcement orders and remediation costs that erode the very savings AI promises; track evolving federal action with legal overviews and legislative trackers to keep deployments both efficient and defensible.

See federal AI laws and hearings for a consolidated summary and an ongoing AI legislation tracker for bill‑level updates.

Policy item	Reference / date
Illinois‑relevant bill (robocall AI disclosures)	H.R.1027 - Rep. Eric Sorensen (D‑IL), introduced 2/06/2025
FTC enforcement example	DoNotPay finalized order - 2/11/2025 (deceptive AI claims)
Technical guidance	NIST AI Risk Management Framework (AI RMF 1.0)

“Congress must play a role – must play a role – in regulating AI.”

Practical next steps for Chicago financial services leaders start with short, governed pilots: require a vendor due‑diligence package and a formal impact assessment before any production rollout (use the practical compliance checklist to prepare disclosures and vendor testing), then run a focused proof‑of‑concept for a single use case such as alternative credit scoring to expand access while preserving fairness (see Zest AI credit‑scoring examples for thin‑file borrowers).

Pair each pilot with targeted reskilling so operations, underwriting and contact‑center teams shift into oversight and exception handling rather than routine tasks - consider cohort training like the Nucamp AI Essentials for Work bootcamp to build prompt‑writing and model‑monitoring skills.

Set concrete KPIs up‑front (for example, containment targets similar to Great Lakes Credit Union's ~60% inbound call handling) and embed explainability, consumer‑facing disclosures and rollback criteria; that compliance‑first, workforce‑led approach captures measurable savings while keeping regulatory and operational risk manageable.