#9: Predicting Energy Bill Defaults, Transforming How We Hire, and Redefining Trading Strategies
π Hello and a warm welcome to Issue #9 of Good AI Vibes! Whether you're a seasoned subscriber or just joining our community, we're thrilled to take you along on this journey of unveiling AI's transformative power. If you haven't already, hit the subscribe button below and join our adventure. π
In every issue, we turn the spotlight on three AI applications that are creating ripples across various sectors. In this edition of Good AI Vibes, we're excited to dive into:
π§Ύ Predicting Energy Bill Defaults (Industry: Energy & Built Environments / Business Function: Financial Management, Risk & Procurement)
π Transforming How We Hire (Industry: Technology, Media & Telecommunications / Business Function: People Management & Organizational Development)
π Redefining Trading Strategies (Industry: Financial Services / Business Function: Operational Efficiency & Production)
Through these stories, we hope to inspire you to imagine the endless possibilities AI holds for your own businesses.
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Predicting Energy Bill Defaults
What's the critical hurdle?
Meet William, a CFO at a mid-sized energy utility company. One of the crucial business challenges he faces is predicting his customers' propensity to pay their energy bills, a task as unpredictable as weather forecasts. The problem with this uncertainty is it can lead to a domino effect, impairing cash flow, increasing bad debts, and even impacting customer satisfaction rates. Imagine allocating resources for debt collection, only to find out that customers were going to pay anyway. Alternatively, imagine not identifying vulnerable customers who need payment assistance, missing an opportunity to enhance customer relationships and goodwill. Such missteps can hit the company's bottom line and reputation significantly.
How was this initially tackled?
Traditional approaches to this problem rely on demographic data and customer payment histories to predict future payment behaviors. For instance, if a customer consistently pays late, it's reasonable to assume this trend might continue. Similarly, certain demographics might be more prone to late payments or defaults. However, this solution isn't foolproof and lacks sophistication, offering only a rough guide rather than a precise forecast.
Why did the initial approach fall short?
The initial solution, while providing some insight, fell short due to its lack of precision and inability to consider unique individual circumstances. Demographics and payment histories are broad strokes in a picture that requires detailed coloring. Individual customer circumstances can fluctuate rapidly, and a one-size-fits-all solution can overlook these variations. Additionally, these models lack an understanding of uncertainty - they predict outcomes without considering the level of confidence in their predictions. This shortfall means that some predictions may be acted upon with unwarranted certainty, leading to inefficient resource allocation and potentially missed opportunities to support customers in need.
How did AI revolutionize the solution?
Enter the transformative power of AI. Williamβs AI-based solution harnessed machine learning to build models predicting customers' propensity to pay their energy bills. It combined a limited number of customer-specific features, like payment history and consumption data, with publicly available census data. Notably, the solution's brilliance lies in the type of AI model that estimates the uncertainty in predictions.
Imagine this: instead of just predicting whether Mrs. Smith will pay her energy bill on time, the AI model also provides an estimate of how confident it is in that prediction. This nuance allows the energy company to make informed decisions based on the level of certainty, optimizing resource allocation.
For instance, for predictions with high certainty, the company could initiate debt collection procedures or provide payment assistance, depending on the predicted outcome. For predictions with lower certainty, the company might opt for a softer approach, like a gentle reminder or further monitoring. This nuanced approach results in more effective decision-making, ensuring the right customers get the right attention at the right time, enhancing customer experience and boosting company performance.
Main reference: Bashar, M. A., Nayak, R., Astin-Walmsley, K., Heath, K. (2023). Machine learning for predicting propensity-to-pay energy bills. Intelligent Systems with Applications, 17, 200176.
See other references at the end.
Transforming How We Hire
What's the critical hurdle?
Imagine you're in the shoes of Jessica, a seasoned Human Resources Director at a multinational corporation. Every year, she and her team grapple with the daunting task of filling thousands of positions across various departments and geographies. With increasing competition, the stakes are high, and every wrong hire costs the company a fortune in time, resources, and productivity. For instance, a recent study suggests that a bad hire can cost an organization up to five times the bad hire's annual salary. Moreover, it's not just about finding the right fit - but doing so quickly and efficiently, and ensuring diversity within the workforce. The dilemma Jessica faces is universal in the business world - how to optimize recruitment decisions to hire the best talent, ensure diversity, and reduce costs?
How was this initially tackled?
Traditionally, Jessica's team has relied on a mix of intuition, experience, and traditional statistical models for hiring. They would analyze a candidate's qualifications, previous experience, and conduct interviews before making a decision. This conventional method, although valuable, is slow and often subject to unconscious bias, which can influence the final decision.
Why did the initial approach fall short?
Despite the team's best efforts, the traditional approach was not without its flaws. First, it was time-consuming, leaving many positions vacant for long periods, affecting the company's productivity. Second, human bias often crept into decisions, leading to a lack of diversity in the workforce. Additionally, this approach didn't always result in successful hires - people who looked perfect on paper sometimes underperformed or didn't fit the company culture. Furthermore, the static nature of traditional statistical models failed to capture the dynamic and evolving nature of recruitment processes, resulting in suboptimal decisions.
How did AI revolutionize the solution?
Enter AI. Specifically, a groundbreaking analytics framework that employs machine learning (ML) and mathematical programming for making optimized recruitment decisions. Let's call this the Intelligent Recruitment Optimizer (IRO). IRO works in two phases. In the first phase, it predicts the success of a recruitment at the individual job placement level. It uses an ML model on a massive dataset comprising a decade's worth of diverse recruitment records. The strength of this model lies in its interpretability, which provides clear, comprehensible insights to HR professionals like Jessica. For instance, with IRO, an HR professional at a tech company, might find that product manager candidates who have a Master's degree in Business Administration, at least four years of experience in a fast-paced startup environment, and proficiency in user experience (UX) design are more successful in driving their product lines forward.
The second phase of IRO provides a global recruitment optimization plan for the entire organization, accounting for multiple factors. Here, the individual predictions are used to devise a holistic hiring strategy, balancing the trade-offs between diversity and recruitment success rates.
The result? IRO was able to outperform traditional methods significantly, improving both diversity and recruitment success rates. For Jessica and her team, this meant a streamlined, more effective recruitment process, less risk of costly bad hires, and a more diverse workforce - a revolutionary solution to their recruitment conundrum.
Main reference: Pessach, D., Singer, G., Avrahami, D., Chalutz Ben-Gal, H., Shmueli, E., Ben-Gal, I. (2020). Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming. Decision Support Systems, 134, 113290.
See other references at the end.
Redefining Trading Strategies
What's the critical hurdle?
Let's consider Zane, the head of a large investment firm. He faces the daunting task of interpreting and acting upon a myriad of corporate disclosures - dense documents, such as form 8-K filings and ad-hoc announcements. These documents can provide important insights into a company's financial health and prospects, but they are notoriously difficult to interpret quickly and accurately. These documents are often filled with complex jargon and hidden implications that require expert analysis. Not only is this a labor-intensive task for his team, but missing or misinterpreting key information can also lead to potentially costly investment errors.
For instance, let's say Zane's firm overlooked a crucial piece of information buried in a 8-K form, leading to a sizeable investment in a company just before its stock price tumbled. This scenario represents a significant loss opportunity, resulting in lower returns for their investors and, consequently, shaking trust in his firm's capabilities.
How was this initially tackled?
Traditionally, firms like Zane's employ a team of financial analysts who manually review these corporate disclosures. These analysts pore over each document, painstakingly extracting any pertinent details that might impact the company's stock price. Their conclusions would then be discussed and interpreted, with their implications factored into the firm's broader investment strategy.
Imagine a corporate analyst spending hours decoding a dense document only to find a single piece of actionable information. It's like fishing in the vast ocean for a single, elusive fish. A crucial part of their work, but undoubtedly a slow and labor-intensive process.
Why did the initial approach fall short?
Despite the expertise and diligence of financial analysts, this traditional approach has several drawbacks. First, it's time-consuming and hence slow to respond to market dynamics. An overlooked nuance in the morning could cost millions by the evening in the fast-paced financial world. Secondly, humans are not infallible. The process is prone to errors and oversights, especially given the sheer volume of information that analysts need to process. Lastly, the approach is not scalable. As the volume of data in corporate disclosures continues to grow, maintaining the same level of thoroughness becomes increasingly challenging.
How did AI revolutionize the solution?
Zaneβs team employed Natural Language Processing (NLP) models to comb through corporate disclosures automatically, extracting key insights and predicting how they might impact stock prices. These models can process volumes of information much more quickly and accurately than humans, providing real-time insights that can be integrated into trading strategies.
Think of it like this: machine learning models read and understand thousands of corporate disclosures simultaneously, distinguishing between neutral, positive, and negative market reactions, which can inform trading decisions. So, instead of a team of analysts decoding one document at a time, Zane has a 'team' of machine learning models working tirelessly to generate actionable insights from countless documents in real-time.
The application of such AI models to this problem has yielded impressive results. In the cases examined, machine learning-driven strategies generated up to 9.34% out-of-sample annualized return, a significant boost to the bottom line. Importantly, these results took into account practical considerations like transaction costs, order clearance periods, and liquidity filtering. Additionally, the models also provided detailed explanations about how they arrived at decisions, aiding understanding and trust in their functioning.
With AI on his side, not only is Zane able to process corporate disclosures quickly and accurately, but also achieves a higher return on investments, boosting his firm's reputation and client trust. AI, thus, becomes the secret weapon in his investment strategy arsenal.
Main reference: Schmitz, H. C., Lutz, B., Wolff, D., Neumann, D. (2023). When machines trade on corporate disclosures: Using text analytics for investment strategies. Decision Support Systems, 165, 113892.
See other references at the end.
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That's a wrap for the ninth edition of Good AI Vibes! We trust that our curated set of AI use cases have piqued your curiosity and kindled new innovative ideas for your business. π‘
Until we meet again, keep the Good AI Vibes flowing. See you next time! π