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3 ways to use machine learning and AI in risk management


Three women of color in a meeting on their computers, perhaps discussing risk management metrics determined with AI or machine learning
With much confusion surrounding AI and machine learning, our chief Fractional COO specialist explains what you need to know.


By 2025, 100% of enterprise companies will leverage an artificial intelligence (AI) system, according to a recent Forrester report. Retail, sales, and marketing organizations have rapidly adopted AI tools to interact with customers – but for fintech and regtech solutions, a key opportunity for AI lies in risk management.


Today's uncertain economy, combined with extensive climate and operational risks, is driving up the workload for risk managers. Increasing international and local regulations regarding climate, business operations, and financial risks bring additional pressures. However, without an efficient way to identify and monitor risks, organizations can struggle to safeguard themselves and their customers against threats.


Technology such as AI tools and machine learning (ML) offer organizations and governments the ability to assess risks rapidly. For risk managers, it's important to understand how these tools work, in what way they can be implemented, and questions regarding AI ethics. Let's learn how to use machine learning and AI in risk management.


If you want some extra reading, we have a blog pot all about what you need to know about risk metrics in risk management.


A 3-D image of a neural network in the shape of a brain, inferring the use of AI
Let's explore the differences and similarities between artificial intelligence and machine learning.

AI vs ML – Artificial Intelligence vs Machine Learning


Before diving into how to use machine learning and AI in risk management, let’s clarify the differences between the two technologies.


An AI program imitates human decision-making by analyzing vast swathes of data and contextualizing the information to act. Organizations use this technology for things like automation, chatbots, and accelerating audits.


Machine learning, meanwhile, is a subset of AI. Essentially, ML enables an AI program to recognize patterns in data. As a result, these algorithms make it easier to predict the likelihood of different outcomes, optimize processes, or flag potential issues.


3 Ways to Use machine learning and AI in Risk Management


1. Risk Assessment

As threats and business risks become more complex, manual risk assessments aren’t fast or accurate enough to prepare and mitigate these challenges. Limited data and time-consuming analyses slow down the process to the point that the threat may have already occurred by the time its potential is identified.


Using AI to automate the risk assessment process accelerates the process and enables risk management teams to focus on mitigation rather than risk identification. Furthermore, ML technology ensures that the algorithm approves over time.


For businesses looking to fortify their operations against climate change or other unsustainable practices, AI-driven risk assessment can be a game changer. Operational risk management software Hyperproof leverages AI for its assessment and monitoring features.


2. Predictive Analysis

The ability to detect patterns via predictive analysis is a game changer across industries. In the fintech realm, this use case can help startups and established organizations better identify fraud, and determine creditworthy users. When it comes to climate-conscious companies, the risk management team can use it to determine the likelihood of climate disasters and how they will affect the supply chain, as well as stress-test particular solutions. Earthscan uses predictive analysis to help organizations identify their exposure to climate risk based on predictive models and streamlines the monitoring and disclosure process.


3. Fraud Detection

Fraudulent transactions are not only becoming more common but also more costly. LexisNexis found in a recent report that financial service firms alone spend $3.64 for every $1 of fraud loss. Fraud harms both businesses and end-consumers as they burden the cost and lose trust.


Technology such as ML and AI makes it possible for firms to rapidly identify suspicious transactions, streamline risk management workflows, and safeguard data. One example is Effectiv, a platform using AI and machine learning to help financial institutions assess and monitor fraud risks.


A photo of a pair of glasses, behind them are two computers with code - perhaps an AI algorithm for risk management.
Are you prepared for risks associated with AI? Read on to find out how to best prepare.


Is AI itself a Risk?


Like any tool, AI, if not used correctly, offers its own set of challenges and risks. For example, AI and ML technology can lead to:

  • Algorithmic bias – Bad data can contaminate the decision-making process for AI. As a result, the algorithm may make faulty connections. A well-known example is Microsoft's AI-powered bot Tay, which had to be taken offline within a day as it began to repeat racist and sexist rhetoric. While this was a chatbot experiment, similar challenges can sneak into hiring or resource allotment programs.

  • Job loss – Another danger with AI is the potential for loss of decent work. Technology like AI and ML are meant to supplement human workers, not replace them. Replacing these works with technology can lead to errors, biases, and other issues within the system.

  • Lack of transparency – To fully understand how AI makes its decisions, it’s necessary to recognize the methodology. But the materials are extremely technical. It’s often challenging for management teams, policymakers, and end-users to understand AI and ML.

  • Privacy violations – Artificial intelligence can filter through thousands of data points in minutes, if not seconds, and while this is a significant advantage, it can also be abused. Organizations or governments can use AI to penetrate personal privacy. For example, they can use the speed and accuracy of AI to track an individual, monitor their relationships, and attempt to predict their actions. When combined with biases, this can become devastating, such as the effect of predictive policing on arrest rates in Black communities.

  • Socioeconomic inequalities – Another potential challenge with AI is unintentional discrimination or silencing of minorities. If AI is trained primarily from one set of the population, it can disenfranchise others across industries and activities, whether it's hiring, applying for a loan, or receiving critical resources.


A man analyzes complex AI-generated data on his computer.

Ethical standards for AI in business


Artificial intelligence and similar technologies continue to evolve as scientists and policymakers set approaches for ethical AI. UNESCO proposes 10 core principles, such as using AI to prevent harm, securing data, promoting inclusivity, and building traceability into AI algorithms.


Startups looking to build and incorporate AI into their solutions should consider the ethics of how they use the technology. Some questions to ask when including AI in your risk management process include:

  • Who owns the data used to train AI, and do we have the right to use it?

  • Is the privacy of data owners protected?

  • Does our data management workflow follow international and local laws?

  • Is there sufficient oversight over the technology?

  • Is it possible to audit or trace how AI reaches its decisions?

  • How does implementing AI, ML, or other technology support human staff?

  • How can we ensure that end-users understand our use of AI?

  • How can we position our workflow to ensure our AI algorithm is inclusive and does not contain biases?


Transforming your organization with risk management


Artificial intelligence and ML technology offer startups and companies across industries the ability to optimize and streamline their risk management systems. Not only can you now safeguard your business against current threats, but you can potentially predict potential risk areas as well.


However, it’s essential to consider technology best practices and AI ethics from the start of your digital transformation.



Bhuva Shakti is an expert in artificial intelligence and machine learning in risk management

Bhuva Shakti has spent over 25 years in fintech, regtech, and digital transformation. You can work with her today to map your risk management process, implement new technology, and discuss more essential strategies for ethically starting or growing your business.


Book a call to hire your new risk management officer and get started today.





 

This blog post can also be found on Bhuva Shakti’s LinkedIn newsletter “The BIG Bulletin.” Both the BIG Bulletin on LinkedIn and the BIG Blog are managed by Bhuva’s Impact Global. We encourage readers to visit Bhuva’s LinkedIn page for more insightful articles, posts, and resources.

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