If chance is associated, outcome cannot be predicted or repeated due to associated uncertain factor and uncertainty known as the random factor. To ignore this unpredictability or uncertainty is equivalent to ignoring the reality. This paper describes some of the commonly used tools for dealing with uncertainty in financial planning and management. Subsequently discussed concepts and tools are used in various types of financial optimization, simulation and statistical models for the prediction and evaluation of probable impact. In general, a probabilistic model where the time factor is associated with the probability in the model is known as a stochastic model. In other words, a stochastic variable or process is probabilistic with time factor. It can be analyzed by using the tools of probability theory.

Stochastic models can reflect real-world economic scenarios that provide a range of possible outcomes. Stochastic modeling presents and predicts outcomes that account for certain levels of unpredictability or randomness with time. In the finance, companies and industries are employing stochastic modeling to improve their business practices and increase profitability. In the financial services, planners, analysts, and managers are using stochastic modeling to manage their inputs and outputs for optimization. Application of this analytical approach is seen in various sectors like the financial market, agriculture, weather forecasting, and manufacturing. Stochastic modeling is also used in the insurance industry, stock investing, biology, demography, and quantum physics. Examples of stochastic models are Probit./Logit  Regression Model, Monte Carlo Simulation, and Markov-Chain Models.

The difference between deterministic and probabilistic modeling concepts is significant. Deterministic modeling gives the same exact results for a particular set of inputs, no matter how many times you re-calculate the model. In this case the mathematical properties are well known. On the other hand, probabilistic modeling is fundamentally random, and the uncertainty is included into the model. The model produces many answers, estimates, and outcomes if chance is involved. This model includes random variables to produce many different outcomes under various circumstances.

This paper explains the concept of derivation and applications of some stochastic modeling approaches for dealing with uncertainty in financial planning, management, agriculture and demography in simple manner especial;;y for business and health professionals.

Keywords: Stochastic Model, Financial Planning, Regression Analysis, Optimization

Dr. Ajay S. Singh is a Senior Lecturer in the Department of Agricultural Economics and Management, University of Swaziland, Luyengo, Eswatini (Swaziland). He completed Graduation and Postgraduation from the Banaras Hindu University (BHU), Varanasi, India; which is one of the reputed global universities. Dr. Singh completed Ph. D. (Preventive Medical Sciences-Statistics) from the Institute of Medical Sciences, BHU on the human fertility behaviour and derived analytical model for the estimation of fecundabilty through application of stochastic approach.

He has taught undergraduate and postgraduate courses in Demography, Econometrics, Mathematical Statistics and Operation Research. He is in an accomplished academic and researcher with more than 20 years of experience in teaching, research and management.

Dr. Singh also taught as a lecturer of Biostatistics in National Institute of Pharmaceutical Education and Research, in Hajipur under the mentorship of Rajendra Memorial Research Institute of Medical Sciences (RMRIMS, ICMR), Patna, India. He has worked for the Indian Council of Medical Research (ICMR), the World Health Organization (WHO), and many prominent non-governmental organizations. Presently, he is also working on an NIH sponsored project with the collaboration of the University of Eswatini, New York University (NYU), the University of York (U.K.) and the Ministry of Health (Eswatini).

Dr. Singh has published more than sixty research papers in several peer-reviewed international and national journals of repute. He has made notable contributions to the health and agricultural research field by serving on various committees and editorial boards of journals. Dr. Singh also worked for a long time in the S. N. Medical College, Agra, India for the cancer prevention programme. His main research interests are in the area of analytical modelling, research methods, data analysis of health, agriculture and rural development in a simple manner.