Optimizing Credit Limit Adjustments Under Adversarial Goals Using Reinforcement Learning

Reinforcement learning has been explored for many problems, from video games with deterministic environments to portfolio and operations management in which scenarios are stochastic; however, there have been few attempts to test these methods in banking problems. In this study, we sought to find and automatize an optimal credit card limit adjustment policy by employing reinforcement learning techniques. In particular, because of the historical data available, we considered two possible actions per customer, namely increasing or maintaining an individual's current credit limit. To find this policy, we first formulated this decision-making question as an optimization problem in which the expected profit was maximized; therefore, we balanced two adversarial goals: maximizing the portfolio's revenue and minimizing the portfolio's provisions. Second, given the particularities of our problem, we used an offline learning strategy to simulate the impact of the action based on historical data from a super-app (i.e., a mobile application that offers various services from goods deliveries to financial products) in Latin America to train our reinforcement learning agent. Our results show that a Double Q-learning agent with optimized hyperparameters can outperform other strategies and generate a non-trivial optimal policy reflecting the complex nature of this decision. Our research not only establishes a conceptual structure for applying reinforcement learning framework to credit limit adjustment, presenting an objective technique to make these decisions primarily based on data-driven methods rather than relying only on expert-driven systems but also provides insights into the effect of alternative data usage for determining these modifications.Comment: 29 pages, 16 figure

Variational quantum Monte Carlo calculations for solid surfaces

Quantum Monte Carlo methods have proven to predict atomic and bulk properties of light and non-light elements with high accuracy. Here we report on the first variational quantum Monte Carlo (VMC) calculations for solid surfaces. Taking the boundary condition for the simulation from a finite layer geometry, the Hamiltonian, including a nonlocal pseudopotential, is cast in a layer resolved form and evaluated with a two-dimensional Ewald summation technique. The exact cancellation of all Jellium contributions to the Hamiltonian is ensured. The many-body trial wave function consists of a Slater determinant with parameterized localized orbitals and a Jastrow factor with a common two-body term plus a new confinement term representing further variational freedom to take into account the existence of the surface. We present results for the ideal (110) surface of Galliumarsenide for different system sizes. With the optimized trial wave function, we determine some properties related to a solid surface to illustrate that VMC techniques provide standard results under full inclusion of many-body effects at solid surfaces.Comment: 9 pages with 2 figures (eps) included, Latex 2.09, uses REVTEX style, submitted to Phys. Rev.

Properties of Saturn Kilometric Radiation measured within its source region

On 17 October 2008, the Cassini spacecraft crossed the southern sources of Saturn kilometric radiation (SKR), while flying along high-latitude nightside magnetic field lines. In situ measurements allowed us to characterize for the first time the source region of an extra-terrestrial auroral radio emission. Using radio, magnetic field and particle observations, we show that SKR sources are surrounded by a hot tenuous plasma, in a region of upward field-aligned currents. Magnetic field lines supporting radio sources map a continuous, high-latitude and spiral-shaped auroral oval observed on the dawnside, consistent with enhanced auroral activity. Investigating the Cyclotron Maser Instability (CMI) as a mechanism responsible for SKR generation, we find that observed cutoff frequencies are consistent with radio waves amplified perpendicular to the magnetic field by hot (6 to 9 keV) resonant electrons, measured locally

Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty

This study explores how researchers’ analytical choices affect the reliability of scientific findings. Most discussions of reliability problems in science focus on systematic biases. We broaden the lens to emphasize the idiosyncrasy of conscious and unconscious decisions that researchers make during data analysis. We coordinated 161 researchers in 73 research teams and observed their research decisions as they used the same data to independently test the same prominent social science hypothesis: that greater immigration reduces support for social policies among the public. In this typical case of social science research, research teams reported both widely diverging numerical findings and substantive conclusions despite identical start conditions. Researchers’ expertise, prior beliefs, and expectations barely predict the wide variation in research outcomes. More than 95% of the total variance in numerical results remains unexplained even after qualitative coding of all identifiable decisions in each team’s workflow. This reveals a universe of uncertainty that remains hidden when considering a single study in isolation. The idiosyncratic nature of how researchers’ results and conclusions varied is a previously underappreciated explanation for why many scientific hypotheses remain contested. These results call for greater epistemic humility and clarity in reporting scientific findings

A missense mutation in the vasopressin-neurophysin precursor gene cosegregates with human autosomal dominant neurohypophyseal diabetes insipidus.

Familial neurohypophyseal diabetes insipidus in humans is a rare disease transmitted as an autosomal dominant trait. Affected individuals have very low or undetectable levels of circulating vasopressin and suffer from polydipsia and polyuria. An obvious candidate gene for the disease is the vasopressin-neurophysin (AVP-NP) precursor gene on human chromosome 20. The 2 kb gene with three exons encodes a composite precursor protein consisting of the neuropeptide vasopressin and two associated proteins, neurophysin and a glycopeptide. Cloning and nucleotide sequence analysis of both alleles of the AVP-NP gene present in a Dutch ADNDI family reveals a point mutation in one allele of the affected family members. Comparison of the nucleotide sequences shows a G----T transversion within the neurophysin-encoding exon B. This missense mutation converts a highly conserved glycine (Gly17 of neurophysin) to a valine residue. RFLP analysis of six related family members indicates cosegregation of the mutant allele with the DI phenotype. The mutation is not present in 96 chromosomes of an unrelated control group. These data suggest that a single amino acid exchange within a highly conserved domain of the human vasopressin-associated neurophysin is the primary cause of one form of ADNDI