Artificial intelligence technologies are redefining how utilities providers approach credit risk management. These cutting-edge solutions integrate advanced data and analytics at all stages of the credit risk lifecycle—from originations to customer management to collections—to optimize outcomes for creditors and customers alike.
This article explores some of the most innovative approaches for applying AI in credit risk management. While it’s focused on utilities providers, it’s worth noting that many of the same use cases apply to other industries looking to adopt credit risk AI, including financial institutions, banks, fintechs, and telecom providers.
The Role of AI in Credit Risk Management
A recent report from Itron found that 86% of utility executives recognize the value of AI and ML technologies in overcoming operational challenges.
Across the globe, utilities are increasingly moving towards a more technologically advanced approach to energy management. Already, 22% of providers report that they’ve “fully integrated” AI and ML technologies into their operations. Another 60% say that they’ve made significant investments in several mature projects.
Today, many utilities providers are using AI analysis to predict and manage energy demand. For example, providers might redistribute resources or leverage dynamic pricing strategies to balance grid load during peak times. That way, they can reduce the risk of power outages without taking on additional resources.
Providers can also use AI to help with maintenance. By proactively monitoring equipment, this technology can identify potential issues and schedule maintenance before it becomes a serious issue. As a result, it helps reduce maintenance costs and minimize the risk of unplanned outages.
In the event of an outage, AI can also help providers detect issues in real time. Continuous monitoring systems support rapid response and restoration, so systems get back online faster, with less of an impact on your customers.
AI-powered tools are incredibly powerful—and they’re incredibly versatile as well. Increasingly, utilities providers are also looking to adopt AI technology for credit risk management. Teams tasked with onboarding new customers, managing existing clients, and handling payments and collections can all benefit from AI in credit risk management.
In the next section, we’ll explore how industry leaders have deployed AI in credit risk to enhance operational efficiency, boost customer satisfaction, improve retention rates, and so much more.
The Top 8 AI Credit Risk Use Cases
Any utilities provider will need to identify, evaluate, and manage the risk associated with a potential customer. An effective credit risk management strategy makes all the difference when it comes to mitigating loss and reducing the risk of defaults.
Fortunately, credit risk AI tools have the power to streamline these processes. These days, they’re making it easier for utilities providers to assess creditworthiness, detect fraud, and collect on unpaid debts.
Let’s explore how cutting-edge providers in the utilities space are operationalizing this technology to impressive results.
#1 - Identity verification
AI-driven solutions automate identity verification processes. These tools cross-reference submitted information with various databases and records in real-time, minimizing the need for manual review. By significantly decreasing processing times, they help utilities providers serve new customers more efficiently.
These tools also play a key role in reducing the risk of identity fraud. AI in credit risk management can identify suspicious activities in submitted documents, such as tampered IDs or mismatched personal details. Flagging these inconsistencies prompts further investigation, helping utilities companies prevent fraudulent accounts from being established in the first place.
#2 - Assessing creditworthiness
In addition to fraud detection, AI plays a major role in assessing customer creditworthiness. Traditional credit assessment methods tend to rely on historical credit scores and financial data, which may not provide a comprehensive view of an applicant's risk profile. AI in credit risk management enhances this process by integrating alternative data sources—such as utility payment history, social media activity, and transaction patterns—to create a more holistic assessment of a customer's financial behavior.
AI systems automate the credit scoring process using advanced algorithms like logistic regression and decision trees. These models evaluate a broader range of factors, allowing utilities providers to make more informed decisions about customer creditworthiness in real time. This capability not only expedites the onboarding process but also helps reduce default risks by identifying high-risk customers early on.
#3 - Fraud detection
AI-powered machine learning algorithms analyze vast amounts of customer data to identify suspicious patterns in real time. For instance, AI in credit risk management can detect anomalies in energy consumption data, including sudden spikes or drops that are inconsistent with a customer's historical usage.
Pacific Gas and Electric Company (PG&E) has already developed an AI-based system to identify potential meter tampering or energy theft. The system analyzes customer data and usage patterns, effectively detecting various fraudulent activities and leading to significant revenue recovery for the company.
#4 - Pre-delinquency
Proactively identify and address potential payment issues before they escalate into serious delinquencies. By leveraging predictive analytics, utilities providers can examine data points like billing histories, payment patterns, and economic indicators. This information supports the creation of detailed risk profiles for each customer, which can identify those at greatest risk of falling behind on their payments.
AI models can segment customers based on their likelihood to pay, allowing providers to tailor their outreach strategies accordingly. By reaching out to high-risk customers with personalized communication—such as reminders or payment plan options—utilities can significantly reduce the chance of delinquency.
#5 - Personalized outreach
Traditional communication methods treat all customers the same, regardless of their individual circumstances or payment history. By adopting AI in credit risk, utilities providers can efficiently analyze vast customer datasets including payment patterns, energy consumption habits, and other publicly available data to gain a better understanding of their customers.
Based on these insights, AI can tailor outreach efforts in several ways. For customers with a history of timely payments, gentle reminders or proactive alerts about unusually high usage can be delivered through their preferred communication channel, such as SMS or email. For those facing financial hardship, AI in credit risk management can identify signs of struggle, such as inconsistent payment patterns or significant changes in energy consumption, and proactively offer personalized payment plans or connect them with energy assistance programs.
#6 - Customer service
AI significantly enhances customer service for utilities providers through the implementation of chatbots and other automated communication systems. These AI-driven solutions are available 24/7, allowing customers to receive immediate assistance with a wide range of inquiries, from billing questions to service interruptions.
By leveraging natural language processing (NLP), chatbots can understand and respond to customer queries in a conversational manner, providing accurate and relevant information without the need for human intervention. Immediate engagement improves customer satisfaction while also reducing the volume of calls directed to human agents, allowing teams to focus on more complex accounts.
#7 - Collections & recovery
Optimize your collections approach with AI. AI-driven algorithms analyze vast datasets, including payment history, consumption patterns, and customer demographics, to predict the likelihood of repayment and segment customers based on risk. Utilities companies can use this information to tailor collections strategies for different customer segments.
Make the most of every customer interaction by leveraging data to deliver the right message through the right channel at the right time. Not only does credit risk AI help more effectively manage collections team resources, but it also encourages higher repayment rates. Deliver more personalized care to your customers to facilitate stronger relationships and lifelong brand loyalty.
#8 - Regulatory compliance
Generative AI in credit risk management helps automate compliance checks. Combined with real-time monitoring tools, these systems reduce the risk of human error by ensuring all interactions with customers are compliant with relevant regulations.
These AI-driven solutions can analyze customer data and payment histories to flag potential compliance issues before they escalate. As an example, AI can monitor communications to ensure that debt collection practices align with consumer protection laws, thereby minimizing the risk of fines or legal repercussions.
AI in credit risk management also enhances documentation management by automating the review of regulatory requirements and ensuring all necessary records are maintained accurately. You can use this technology to generate reports demonstrating compliance with billing regulations, making it easier to respond to audits and regulatory inquiries.
The future of AI in credit risk management
A 2024 survey from McKinsey revealed that 80% of surveyed credit risk organizations expect to implement AI technologies within the next year.
Despite the significant enthusiasm, participants in that same survey also acknowledged that there are some difficulties presented when implementing this technology at scale. Notably, 75% of respondents identified risk and governance concerns as the most significant barrier to widespread AI adoption within their organization.
Today’s regulatory landscape has placed an increased emphasis on data security when using customer’s personally identifiable information (PII). As a result, security, transparency, and fairness are critical considerations for any utilities provider looking to incorporate AI in credit risk management.
Following industry-standard security procedures and best practices is the best way for providers to minimize risk while maximizing the value of advanced AI technology. Today, industry leaders are setting the standard for data security measures when using high-quality information to train their cutting-edge AI models.
Credit risk AI: Data security
Ensure adherence to the strictest data security standards by implementing robust data protection measures like encryption, access controls, and regular security audits. Zero-trust architectures and AI-driven anomaly detection systems help providers safeguard sensitive customer information when using this advanced technology.
Even better, as AI becomes more and more sophisticated, it’s proving itself to be one of the most effective tools for enhanced cybersecurity. This technology can be leveraged to predict and prevent incidents before they escalate by continuously monitoring and analyzing data. As a result, utilities providers can use AI to identify vulnerabilities and respond swiftly to potential threats.
Credit risk AI: Data quality
Mitigate risk by ensuring high-quality data is used to train AI models. Utilities providers can implement rigorous data governance frameworks and advanced data validation techniques to establish a comprehensive data governance strategy. Clear policies around data collection, storage, and usage ensure all data is accurate, consistent, and up to date.
Providers may also find it useful to perform periodic data quality checks. Partnering with third-party data providers helps enhance the richness and reliability of the datasets used. That way, utilities can ensure greater transparency, fairness, and explainability with AI in credit risk management.
C&R Software operationalizes AI in credit risk management
Trusted by industry leaders in more than 60 countries worldwide, C&R Software’s credit decisioning software is an experienced provider of AI-native solutions for credit risk management. To date, our clients include electric, water, natural gas, and countless other utilities providers.
Leverage our FitLogic decision platform to make better decisions at every stage of the credit risk lifecycle. Advanced, AI-driven insights provide utilities companies with a deeper understanding of their customers to maximize outcomes at every touchpoint. Transform inefficient credit processes with a robust solution designed to cope with strict regulatory requirements and data security standards.
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