In this paper, we examine financial literacy in Singapore. Using data from the SKBI-GFLEC Sustainable Investment Survey, we find that approximately 40% of Singaporeans are financially literate. We also find that financial literacy is low among specific groups such as women, the less educated, and the unemployed. Moreover, we examine financial literacy across Environmental, Social, and Governance (ESG) literacy. We find that those with higher ESG knowledge are also more financially literate, showing that knowledge of these two different financial concepts is positively correlated. Finally, our results show that financial literacy can be linked to investment behavior which may affect financial well-being in the long run.

Artificial Intelligence (‘AI’) technologies present great opportunities for the investment management industry (as well as broader financial services). However, there are presently no regulations specifically aiming at AI in investment management. Does this mean that AI is currently unregulated? If not, which hard and soft law rules apply?

Investments are a heavily regulated industry (MIFID II, UCITS IV and V, SM&CR, GDPR etc). Most regulations are intentionally technology-neutral. These regulations are legally binding (hard law). Recent years saw the emergence of regulatory and industry publications (soft laws) focusing specifically on AI. In this Article we analyse both hard law and soft law instruments.

The contributions of this work are: first, a review of key regulations applicable to AI in investment management (and oftentimes by extension to banking as well) from multiple jurisdictions; second, a framework and an analysis of key regulatory themes for AI.

The intensification of Brazil's beef cattle production system can involve different strategies to increase beef production while reducing deforestation in the Amazon biome and mitigating climate change. This study economically evaluates a cooperating beef farm in the state of Mato Grosso, Brazil's Amazon biome over three crop years (2015–16 to 2017–18), transitioning from an extensive grazing system to a semi-intensive system using five sustainable agricultural intensification (SAI) practices. These five practices include (1) grain supplementation for cattle, (2) pasture fertilization, (3) pasture re-seeding, (4) crop–livestock integration (CLI) and (5) irrigated and fertilized pasture that is rotationally grazed. The relative costs of these five SAI strategies used on this cooperating farm are compared. The adoption of SAI strategies increased beef productivity 5.7% (228–241 kg live-weight sold per hectare) and gradually improved net farm income by ~130% over the 3 years of transition (−US$94.79 to $29.80 ha−1). Grain supplementation (US$188 ha−1) had the cheapest cost per hectare, followed by pasture fertilization (US$477 ha−1) and pasture reseeding (US$650 ha−1). The most costly practice was in-ground irrigation of fenced rotationally grazed pasture (US$1600 ha−1) with the second most costly being CLI (US$672 ha−1). Despite adoption challenges of these SAI practices, past research confirm these five practices can increase beef productivity and profitability while reducing carbon footprint. Regardless of the cost per hectare of each practice, farmer adoption can be improved through education, support and incentives from both the public and private sectors.

Life insurers have historically relied upon investment markets as a key source of profit and crucially have been able to do this while embarking on relatively “vanilla” investment strategies. In the current low-yield environment, broadening their investment horizons is critical to maintaining profitability. This paper summarises some relevant external literature and the working party’s own research in understanding the potential benefits and pitfalls for insurers seeking to invest in non-traditional assets. The objective of this paper is to help educate and promote understanding by all (the many) relevant parties. In doing so, we hope to help organisations to achieve some further economic success for the ultimate benefit of society. Although this paper has primarily been written from the perspective of a life insurer, we hope it will be of interest to a much wider audience. Many of the asset classes considered here are relevant to general insurers, pension funds and the wider capital markets. It is very important to note that this paper does not contain investment advice and the analysis represents the views of the individuals and the working party and not the companies which they represent or the Profession. This paper does not make any comment as to the suitability (or otherwise) of specific investments for particular investors.

The process of growth and development of any business entity is tightly linked to investments. This paper elaborates on the application of multiple criteria methods from the Analytic Hierarchy Process (AHP) with the aim to create assumptions for the development of production, particularly focused on opportunities for investments in table egg production. There were two alternatives analysed: investment in enriched cages and investment in alternative production systems. Each of these options had advantages and disadvantages, that were graded by criteria. Investment in alternative systems was assessed as a better alternative, as eggs produced in these systems were estimated to achieve higher income. Financial investments in such systems are higher, but eggs achieve a higher price in the market than those from enriched cages. Sensitivity analysis presented the effects that changes in the egg price and in costs structure have on the net present value of investment. Sensitivity analysis showed that changes in egg price significantly affect the success of business, while input costs have less impact on the business result.

The primary objective of the paper is to explore using reinsurance as a risk management tool for an insurance company. We consider an insurance company whose surplus can be modeled by a Brownian motion with drift and that the surplus can be invested in a risky or riskless asset. Under the above Black-Scholes type framework and using the objective of minimizing the ruin probability of the insurer, we formally establish that the excess-of-loss reinsurance treaty is optimal among the class of plausible reinsurance treaties. We also obtain the optimal level of retention as well as provide an explicit expression of the minimal probability of ruin.

This paper is concerned with the methods of mathematical modeling of thesocio-economicdevelopment of regions. The objective of this study is to analyze and assess thesocio-economicpotential of the territory having regard to its individual socio-economic andeconomic-geographicalcharacteristics. The territory's individual characteristics, in turn, aretreated as invariantconditions of its development. The study territory includes regions and federaldistricts of theRussian Federation.
An assessment of the regions' development potential is made from the perspectiveof analyzingtheir current socio-economic situation for the time interval 1999-2002 takenfrom official statisticalhandbooks. To accomplish this, a special-purpose mathematical model is developedin the formof a hierarchical fractal indicative function relating the generalized indicatorcharacterizing theregions' development potential, or the “indicated” (that which is indicatedversus indicator), to theparticular socio-economic characteristics of the territory, or the developmentindicators of the localeconomy, via the model coefficients which is termed regulators.
As a result, the economic-mathematical model is generated in the form of anindicative functioncharacterizing the current socio-economic situation of the regions and federaldistricts of Russiaon different levels of economic organization (from local and regional tonational). It reflects appropriatelyand dramatically the specific character of the region's socio-economic potentialby theexample of the connection of investments with the volumes of production. Themodel permitsthe position of the regions and districts to be identified in the system ofinter-regional and nationalsocio-economic links and economic-geographical conditions of development. Theresultsderived from modeling the socio-economic development potential of Russia'sregions have beenused to construct the control surface of the country's economy characterizingthe changes in the development potential of the federal districts toward itsdecrease.