Brian Shappell, CBF, Director of Content Strategy
29 February, 2024
In the realm of contemporary business dynamics, autonomous finance has emerged as a transformative force reshaping the landscape of risk assessment and accounts receivable (AR) portfolio management. This paradigm shift transcends conventional human-centric methodologies, ushering in a new era defined by data-driven precision. In the imperative context of rapid decision-making, Artificial Intelligence (AI) assumes a pivotal role in AR management and risk assessment, amplifying organizational agility.
The advanced algorithms inherent in AI, particularly within the realm of generative AI models, elucidate invaluable insights derived from processed data. These algorithms excel in discerning patterns and predicting trends, empowering receivable teams to optimize customer payment flows. In a proactive stance, AI facilitates the preemptive management of potential payment defaults, thereby contributing significantly to risk mitigation and enhancing overall cash flow efficiency.
AR Portfolio Management, in essence, orchestrates a systematic process that involves the meticulous grouping, categorization, and formulation of strategies to guide the management of distinct customer segments. This comprehensive approach extends beyond mere categorization; it encompasses vigilant monitoring and strategic management of diverse customer receivables. By doing so, organizations can strategically navigate the intricate landscape of maximizing cash flow while prudently mitigating risks.
In the ever-evolving landscape of financial management, the integration of autonomous finance augments the decision-making prowess of AR teams, propelling organizations toward resilient and proactive management of their portfolios.
In a recent Gartner webinar poll of more than 2,500 executives, 38% of participants considered customer experience and retention as the primary purpose of their Generative AI investments, followed by revenue growth (26%), cost optimization (17%) and business continuity (7%). These elements are relevant for portfolio management.
Understanding Generative Artificial Intelligence
The emergence of ChatGPT and Bard has already rendered Generative AI (GenAI) an office buzzword, if not a cultural and household one. Simply defined, GenAI is a subset or variant of AI that enables the generation of new content through texts, images, video, audio, or other formats. It uses advanced algorithms to identify underlying patterns in the source content material and blend these identified patterns with their interpretations to produce unique and representative content and artworks. Three-quarters of executives (77%) surveyed by KPMG in May 2023 cited generative AI as “by far the most impactful technology they will use,” with 71% planning “to implement their first generative AI solution within two years.”
Role of GenAI in AR Portfolio Management
GenAI leverages data analytics to gain precise insights that can enable the receivables team to collect payments on time through the use of predictive analytics, risk assessment, and customer behavior analysis. The technology can minimize manual tasks,create efficiencies, and generate personalized content for customers based on their preferences, past behavior, and demographics, resulting in higher engagement with customers.
A Deloitte study says businesses are excited about the potential opportunities of GenAI that offers human-like intelligence and capabilities far beyond human level cognition in certain tasks. It predicts that GenAI will impact every industry and functions and it is estimated that by 2025, 10% of all data produced will be generated by AI (up from less than 1% in 2021). Let us delve into how AI, particularly GenAI, plays a pivotal role in AR management and risk assessment. A generative AI acts as an intelligent virtual assistant that proactively identifies and proactively makes measures to mitigate the payment risks and improve cash flow.
The use cases of GenAI in AR portfolio management includes the following:
- Automating manual tasks: Generative AI helps reduce, if not eliminate time-consuming and error-prone manual data entry in receivable management. The GenAI helps optimize dunning strategies by creating customized and personalized communication with debtors, bringing higher response rate and improvement in customer relationships. This is achieved through learning from payment history and interaction patterns and tailoring the customer approach to individual behaviors and preferences, resulting in increased willingness to pay on time.
- Monitoring accounts in real-time: The generative AI can monitor accounts in near real-time and provide up-to-date information on invoice status, payments made, and outstanding balances. The capabilities of GenAI of attention to detail and 24/7 availability contribute to improved customer trust and loyalty.
- Gaining customer insights: The technology can help companies identify trends and track financial data, allowing receivables teams to make more informed, data-driven decisions. This allows decision-makers to focus on new revenue-generating opportunities, remain nimble in a fluctuating market, and focus on short- and long-term cash planning.
- Data quality: The quality of data is critical for maintaining accuracy in collections processes, including payment reminders. A powerful generative AI can analyze customer master data, payment history and interaction patterns to help manage address payment reminders precisely and individually. The accuracy of data gained through identification and elimination of errors can optimize collection processes including dunning and improve cash flow..
In nutshell, businesses can leverage GenAI to identify customer payment activity patterns, predict payment behavior accurately, and forecast cash flow.
The Role of GenAI in Risk Assessment
Portfolio and Asset management inherently involves risks, and generative AI plays a decisive role in assessing and managing these risks. AI algorithms can analyze various risk factors, such as market volatility, economic conditions, and geopolitical affairs, to provide real-time risk assessments. This enables CFOs, chief investment officers, and asset managers to make dynamic decisions to safeguard investments and minimize potential losses.
The modern business environment is complexly woven with multiple parties and stakeholders, making the relationship with them critical. In this scenario, the correlated risks increase exponentially, making risk assessments a compelling aspect of corporate due diligence. Generative AI plays a transformative role in this framework of interdependencies to refine risk assessments and as well improve their accuracy and depth.
GenAI employs sophisticated algorithms to produce insights and predictive models from large volumes of data it processes to identify accurate risk signals, forecast future behavior of market and economy, and simulate probable risk scenarios, employing the following methods.
- Data Collection: GenAI tools have the capability to aggregate and synthesize data and information from a plenitude of sources, including news channels, databases, and industry studies and reports. This ensures that the range and extent of data analyzed is far beyond what is probable in manual process, while accelerating the data collection process.
- Predictive Modeling: Generative AI leverages machine learning to predict inherent risks that can arise in the future depending on historical data and evolving trends.
- Risk Simulation: The technology can replicate several risk scenarios by generating data-driven models to help understand how a probable risk could develop and impact the business and how to mitigate the risks proactively.
- Risk Monitoring: The traditional assessment approach is static whereas the generative AI follows the model of perpetual monitoring of the elements, providing real-time signals to changes in risk profiles.
In summary
Undoubtedly, the transformative potential of GenAI is poised to revolutionize operational frameworks, particularly in automating critical processes, risk identification, and enhancing overall efficiency within Accounts Receivable management. The prowess of GenAI lies in its adeptness at analyzing vast datasets and accurately predicting trends, thereby streamlining the Order-to-Cash process. Finance leaders can harness the power of Generative AI to gain profound insights into customer behavior and preferences, thereby enabling the development of superior products and services.
GenAI has emerged as a focal point of interest, garnering widespread attention for its capacity to revolutionize various sectors. The accelerated adoption of Generative AI is unsurprising, given its multifaceted applications that promise heightened productivity, seamless access to knowledge, and a substantial anticipated total economic impact ranging from $2.6 trillion to $4.4 trillion annually, as per McKinsey’s findings. In light of such advancements, organizations are well-advised to consider immediately incorporating innovative Generative AI solutions,
exemplified by offerings such as those provided by Emagia, to proactively address risk management challenges and position themselves for sustained growth, scalability, and profitability.
FAQs
How is Generative AI different from “Regular” AI?
AI (Artificial Intelligence) is a broad term that comprises any technology or system capable of performing tasks that typically require human intelligence, that include problem-solving, decision-making, and pattern recognition. Generative AI is a subset of AI that focuses on creating new content (such as images or text) rather than just analyzing existing information. Generative AI models use neural networks to identify the patterns and structures within existing data to generate new content that is similar but unique.
What is Generative AI in Cash Flow?
By identifying and analyzing historical customer payment data and trends, generative AI can help predict how much cash will be coming in and going out. This helps businesses plan and make more informed decisions, which can improve financial stability.
What is the Role of Generative AI in Risk Assessment and Management for AR Portfolios?
Generative AI models produce synthetic data to help AR managers in evaluating various risk factors. By identifying the probability of default and delay from analysis of historical trends, the technology can provide insights into the risks of each customer account and help plan mitigation strategies..
