Risk management is a foundational pillar of the financial services industry.

With the increased usage of artificial intelligence (AI), financial institutions and enterprises can access optimized solutions to flag, monitor, and manage risks in banking & finance.

As 2025 kicks off, AI-powered risk management is becoming a standard mode of operation made possible by nimble fintechs (such as Alloy, Persona, Socure, Sardine, and Kobalt Labs).

At the organizational level, AI enhances a company’s ability to predict market movement, identify fraud, adhere to regulation, and improve decision-making.

In part 3 (of 4) of our series on AI in Financial Services, we breakdown:

• AI’s growing influence in risk management;

• Challenges in applying AI to risk & compliance operations;

• How to overcome these challenges and launch new solutions;

• Case studies and future trends to look out for.

AI’s Role in Revolutionizing Risk Management

AI completely changes financial services by transforming risk management.

Best-in-class analytics, real-time assessments, and predictive findings are available today through artificial intelligence.

Here are the five categories in risk & compliance where AI makes the most impact:

Analytics for Risk Mitigation

AI thrives in consuming volumes of actual data and delivering predictive analytics concerning future risk.

These analytics identify suspicious patterns and flag outlier data in order to forecast industry trends, evaluate multiple types of risks, and detect program weaknesses.

One example comes from machine learning models able to measure the probability of loan defaults through analysis of user behavior & demographics. Financial institutions can then take cautionary steps to adjust product scope and risk exposure.

Advanced Fraud Detection

Besides analytics, fraud detection is a top application of AI for risk & compliance teams at enterprises, banks, and fintech companies.

Current fraud products capture bad actors and unauthorized activity ‘after-the-fact’ — once an attempt has been made. By that time, losses can increase for customers & banks.

New AI-powered solutions identify AND remediate threats in real time with capabilities such as natural language processing (NLP) to (i) detect unusual activity patterns, (ii) flag malware & phishing attempts, and (iii) block suspicious account activities.

The application of AI also minimizes the frequency of false positives, which bolsters the efficiency of a fraud prevention program.

Risk Assessment

Volatility in the market can be a blocker to growth for banks and enterprises.

Besides analyzing volumes of data, AI systems can handle different types of data — for market studies, this includes economic signals, world events, and industry trends.

The latest enhancements for assessments are able to generate sentiment analysis utilizing news network and top opinions from social media platforms. These findings are helpful for investment groups monitoring market conditions closely.

Adherence with Regulatory Compliance

Monitoring evolving regulatory requirements is a challenge (even for the largest companies).

AI simplifies the scope of monitoring with automation for compliance reviews, transaction verification, and detecting breaches.

The latest AI solutions:

• Enable analysis for anti-money laundering (AML) policies;

• Confirm Know Your Customer (KYC) rules are being followed;

• Produce reporting on an as-needed basis with limited, manual involvement.

Benefits from automation include cost-cutting, improved accuracy, and reduce time to fulfillment.

Cybersecurity Risk Management

The dependency on online & mobile experiences has expanded exposure to cybersecurity risk.

Similar to fraud detection enhancements, artificial intelligence improves monitoring efforts within networks and recognizes system weaknesses before they become vulnerabilities.

AI products focused on cybersecurity are able to (i) identify and take action against ransomware threats, (ii) detect insider attacks by analyzing behavior patterns, and (iii) forecast when breaches may occur based on historical data.

When it comes to safeguarding platforms, all companies (including financial institutions) can benefit from more AI-powered programs.

Challenges in AI-Driven Risk Management

The complexities with AI integration are consistent across the board, when it comes to data management and avoiding bias.

For risk management, there’s additional nuance to consider as banks & enterprises build / implement AI solutions:

• Data quality: The most critical input for AI models — complete and accurate data produces effective analysis for decision-making;

• Model Interpretability: Keeping model constructs clear & transparent is important as organizations (and their auditors) must be able to interpret how decisioning is made;

• Replacing Legacy Systems: Many enterprises and banks that utilize outdated infrastructure are unable to benefit from AI or other innovative tech (see our recent article on next-gen infra for a deep dive). Upgrading systems is mandatory, but gathering executive support for change management remains a significant challenge.

• Addressing Ethical Issues: Part 2 of this series explored this topic in detail. Concerns of transparency, responsibility, and fairness become even more crucial in a risk management setting in which regulation is looming. The downstream impact in poor ethical oversight can result in penalties and reputational damage.

• AI Expertise: The talent needed to integrate AI-powered risk management is limited globally. Organizations unable to hire and retain these skilled workers will suffer delays in deployment.

Strategies for Deploying AI solutions

In overcoming these challenges, both financial institutions & enterprises can start with the following five, targeted strategies:

1. Improving Data Governance

Resilient frameworks for data governance deliver consistent & secure data inputs for AI models.

Top practices in this area include (i) standard formatting of data, (ii) frequent data audits for completeness and accuracy, (iii) and integrating strong encryption and access features.

2. Committing to Explainable AI

Explainable AI (XAI) techniques focus on increasing the ‘interpretability’ of AI — how external, non-tech parties are able to comprehend the controls of AI systems.

This requires not only simplifying models, but also improving documentation available post-deployment for training stakeholders and informing auditors.

3. Adopting Modern IT Infrastructure

Legacy systems are unable to support the implementation of artificial intelligence.

Financial institutions need to make the move to cloud-native infra & APIs in order to enable data access and insights.

4. Connecting with Regulators

Collaborating with regulatory agencies as early as possible helps create sustainable & compliant AI solutions.

This can be facilitated through (i) involvement with trade associations / industry groups, (ii) crowd-sourcing best practices towards high ethical standards, and (iii) rallying support for transparent regulation with AI.

5. Creating Skilled Staffing

Since attracting existing AI talent is so challenging, an emphasis should be placed on upskilling and development.

To bridge the workforce gap, enterprises and financial institutions should (i) connect with universities & research firms to gain educational content, and (ii) provide training for current workers interested in developing AI expertise.

Real World Examples and More to Come from AI + Risk

Industry leaders are diving deep in to AI applications for risk management.

JPMorgan Chase leverages AI-driven solutions to identify and block fraud by tracking patterns in real time. The result: JPMC slashed losses due to fraud and enhanced customer satisfaction.

HSBC deploys AI to beef up AML compliance processes — analysis of high transaction volumes on a daily basis enables detection of suspicious behavior and confirms alignment with global regulations.

Goldman Sachs leverages AI systems to gauge risks in the market and improve trading playbooks. By incorporating insights generated by AI, Goldman greatly increased portfolio returns.

Besides these examples of market proof, further innovation in AI + Risk is anticipated over the next 5 years.

The integration of quantum computing is becoming a reality and ushers in a new frontier of resolving challenges with analyzing large, varied sets of data in a rapid way.

Stress testing is a mandatory part of risk management today. AI-driven stress testing allows for multi-scenario & variable conditions which produce findings and remediation plans in an expedited manner — improving a lifecycle that currently takes weeks.

As more enterprises and financial institutions deploy AI solutions, the ability to exchange learnings and best practices will increase. Collaborative AI environments will enable organizations, regulators, and vendors to improve solutions at an industry-level.

Embracing AI for a Resilient Future

The benefits and business case for AI-powered risk management is clear: enhanced efficiency & accuracy, improved adherence to regulatory compliance, more innovation on the horizon, and a sustainable competitive advantage in overall risk management.

As the financial services industry maneuvers more challenges in managing multiple types of risk, artificial intelligence provides a stable path to building and improving policies and controls in place.

Collaboration among numerous parties would fuel proficiency towards best-in-class models and enable a dynamic framework withing banking and finance sectors globally.

The path to AI-driven risk management is not without obstacles, but the rewards are well worth the effort.

Financial insitutions & enterprises willing to start this path in 2025 and overcome early challenges will benefit in the long-term from resiliency and robustness in their risk & compliance programs.

Next up we have the series finale (part 4): “AI and the Future of Finance: Opportunities and Challenges.”

If you missed our earlier coverage, here’s part 1: “How AI Redefines Financial Services in 2025.”

And part 2: “Responsible Innovation: Ethical AI Adoption in Finance.”