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Mapping borrowers’ and lenders’ interactions according to their dark financial profiles

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Abstract

In this interdisciplinary, conceptual article with implications in marketing financial products and services, we study real estate and capital markets characterized by a predatory paradigm and economic agents’ dark financial profiles (DFPs). These are estimated by three orthogonal components—disconnection, irrationality, and deceit. We identify the best interactional patterns of borrower-lender profiles, ones that expectedly minimize the risk of default. We resort to discretized, predator–prey Lotka–Volterra equations where lenders act as predators and borrowers as prey, incorporating market trends and learning effects. To mathematically operationalize our framework, we use combinatorics with high, medium, and low levels of the three components of DFPs. We find 27 salient lender-borrower interactional scenarios and observe three different patterns: explosive, conducive, and implosive. Our theoretical findings indicate that equal (ir)rationality (in financial terms) between lenders and borrowers is a necessary but insufficient condition to maintain harmonious, long-term relationships. We use eutectic theory to map the agents’ profiles by introducing another variable: Expected return [E(Rp)] versus risk [σ], using the Capital Asset Pricing Model (CAPM) as a base. We find six market segments: the inactive predators and prey, the loose, the greedy, the vulnerable, and the stable. We identify the optimal combination of borrowers–lenders interaction under risk, given market trends and learning effects. We propose a path for future research that would see the application of analytical tools such as factor analysis, k-means clustering algorithm, χ2 and non-parametric Kruskal–Wallis and Dunn’s multiple comparison tests to verify differences among the hypothesized segments.

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Notes

  1. Developed by the Fair Isaac Corporation to assess credit worthiness.

  2. In marketing, this could be approximated as a life-stage line, as US consumers typically improve their standards of living with age, and tend to invest more accordingly.

  3. The well-established formula is: E(Rp) = Rf + β · (ERm − Rf) where: E(Rp) = expected return of the investment portfolio, Rf = risk-free rate, β = beta of the investment, ERm = expected market return, (ERm − Rf) = market risk premium.

  4. These numbers are like the well-known Pareto 80–20 ratio.

  5. Note that the model cannot go beyond the limits set by the semi-dotted vertical lines all around the figure. For example, on the far left, the excess of k will lead to too many predatory behaviors, which will eventually mean the death of all the prey, which are the predators’ source of survival. On the far right, as k approaches zero, the population of predators is overwhelmed by the growth of the prey behaviors, which will exhaust all resources that sustained the prey’s lives. At both extremes, the economic ecosystem crashes.

  6. Of note, borrowers are not consumers, at least not until they buy a product. Hence, this article is about borrowers’ behaviors, not consumers’ behaviors.

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Acknowledgements

We thank professor Silvester Ivanaj for his assistance in preparing this article.

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    Olivier Mesly & Hareesh Mavoori

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Appendix: Summary of the interactional DFP scenarios

Appendix: Summary of the interactional DFP scenarios

See Table 3.

Table 3 Summary table (by patterns and asymmetry)
Full size table

The scenario set by [o = O, r = R, d <  < D] offers the best possibility among the six scenarios of the mutually beneficial interactions. Both the lender and the borrower evolve, and they evolve equally and with optimal mutual benefits.

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Mesly, O., Mavoori, H. Mapping borrowers’ and lenders’ interactions according to their dark financial profiles. J Market Anal (2023). https://doi.org/10.1057/s41270-023-00263-1

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