Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms

Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms Description

MCA Technology Solutions Private Limited was established in 2015 in Bangalore with an objective to integrate analytics and technology with business. MCA Technology Solutions helped its clients in areas such as customer intelligence, forecasting, optimization, risk assessment, web analytics, and text mining and cloud solutions. Risk assessment vertical at MCA technology solutions focused on problems such as fraud detection and credit scoring. Sachin Kumar, Director at MCA Technology Solutions, Bangalore was approached by one his clients, a commercial bank, to assist them in detecting earnings manipulators among the bank's customers. The bank provided business loans to small and medium enterprises and the value of loan ranged from INR 10 million to 500 million. The bank suspected that its customers may be involved in earnings manipulations to increase their chance of securing a loan. Saurabh Rishi, the chief data scientist at MCA Technologies was assigned the task of developing a use case for predicting earnings manipulations. He was aware of models such as Benford's law and Beneish model used for predicting earnings manipulations; however, he was not sure of its performance, especially in the Indian context. Saurabh decided to develop his own model for predicting earnings manipulations using data downloaded from the Prowess database maintained by the Centre of Monitoring Indian Economy (CMIE). Daniel received information related to earning manipulators from Securities Exchange Board of India (SEBI) and the Lexis Nexis database. Data on more than 1200 companies was collected to develop the model. MCA Technology believed that machine learning algorithms may give better accuracy compared to other traditional models such as Beneish model used for predicting earnings manipulation.

Case Description Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms

Strategic Managment Tools Used in Case Study Analysis of Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms

STEP 1. Problem Identification in Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms case study

STEP 2. External Environment Analysis - PESTEL / PEST / STEP Analysis of Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms case study

STEP 3. Industry Specific / Porter Five Forces Analysis of Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms case study

STEP 4. Evaluating Alternatives / SWOT Analysis of Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms case study

STEP 5. Porter Value Chain Analysis / VRIO / VRIN Analysis Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms case study

STEP 6. Recommendations Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms case study

STEP 7. Basis of Recommendations for Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms case study

Case Analysis of Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms

Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms is a Harvard Business (HBR) Case Study on Finance & Accounting , Texas Business School provides HBR case study assignment help for just $9. Texas Business School(TBS) case study solution is based on HBR Case Study Method framework, TBS expertise & global insights. Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms is designed and drafted in a manner to allow the HBR case study reader to analyze a real-world problem by putting reader into the position of the decision maker. Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms case study will help professionals, MBA, EMBA, and leaders to develop a broad and clear understanding of casecategory challenges. Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms will also provide insight into areas such as – wordlist , strategy, leadership, sales and marketing, and negotiations.

Case Study Solutions Background Work

Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms case study solution is focused on solving the strategic and operational challenges the protagonist of the case is facing. The challenges involve – evaluation of strategic options, key role of Finance & Accounting, leadership qualities of the protagonist, and dynamics of the external environment. The challenge in front of the protagonist, of Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms, is to not only build a competitive position of the organization but also to sustain it over a period of time.

Strategic Management Tools Used in Case Study Solution

The Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms case study solution requires the MBA, EMBA, executive, professional to have a deep understanding of various strategic management tools such as SWOT Analysis, PESTEL Analysis / PEST Analysis / STEP Analysis, Porter Five Forces Analysis, Go To Market Strategy, BCG Matrix Analysis, Porter Value Chain Analysis, Ansoff Matrix Analysis, VRIO / VRIN and Marketing Mix Analysis.

Texas Business School Approach to Finance & Accounting Solutions

In the Texas Business School, Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms case study solution – following strategic tools are used - SWOT Analysis, PESTEL Analysis / PEST Analysis / STEP Analysis, Porter Five Forces Analysis, Go To Market Strategy, BCG Matrix Analysis, Porter Value Chain Analysis, Ansoff Matrix Analysis, VRIO / VRIN and Marketing Mix Analysis.

We have additionally used the concept of supply chain management and leadership framework to build a comprehensive case study solution for the case – Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms

Step 1 – Problem Identification of Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms - Harvard Business School Case Study

The first step to solve HBR Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms case study solution is to identify the problem present in the case. The problem statement of the case is provided in the beginning of the case where the protagonist is contemplating various options in the face of numerous challenges that Mca Manipulations is facing right now. Even though the problem statement is essentially – “Finance & Accounting” challenge but it has impacted by others factors such as communication in the organization, uncertainty in the external environment, leadership in Mca Manipulations, style of leadership and organization structure, marketing and sales, organizational behavior, strategy, internal politics, stakeholders priorities and more.

Step 2 – External Environment Analysis

Texas Business School approach of case study analysis – Conclusion, Reasons, Evidences - provides a framework to analyze every HBR case study. It requires conducting robust external environmental analysis to decipher evidences for the reasons presented in the Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms.

The external environment analysis of Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms will ensure that we are keeping a tab on the macro-environment factors that are directly and indirectly impacting the business of the firm.

What is PESTEL Analysis? Briefly Explained

PESTEL stands for political, economic, social, technological, environmental and legal factors that impact the external environment of firm in Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms case study. PESTEL analysis of " Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms" can help us understand why the organization is performing badly, what are the factors in the external environment that are impacting the performance of the organization, and how the organization can either manage or mitigate the impact of these external factors.

How to do PESTEL / PEST / STEP Analysis? What are the components of PESTEL Analysis?

As mentioned above PESTEL Analysis has six elements – political, economic, social, technological, environmental, and legal. All the six elements are explained in context with Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms macro-environment and how it impacts the businesses of the firm.

How to do PESTEL Analysis for Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms

To do comprehensive PESTEL analysis of case study – Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms , we have researched numerous components under the six factors of PESTEL analysis.

Political Factors that Impact Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms

Political factors impact seven key decision making areas – economic environment, socio-cultural environment, rate of innovation & investment in research & development, environmental laws, legal requirements, and acceptance of new technologies.

Policy Making Impact on Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms

Government policies have significant impact on the business environment of any country. The firm in “ Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms ” needs to navigate these policy decisions to create either an edge for itself or reduce the negative impact of the policy as far as possible.

Data safety laws – The countries in which Mca Manipulations is operating, firms are required to store customer data within the premises of the country. Mca Manipulations needs to restructure its IT policies to accommodate these changes. In the EU countries, firms are required to make special provision for privacy issues and other laws.

Competition Regulations – Numerous countries have strong competition laws both regarding the monopoly conditions and day to day fair business practices. Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms has numerous instances where the competition regulations aspects can be scrutinized.

Import restrictions on products – Before entering the new market, Mca Manipulations in case study Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms" should look into the import restrictions that may be present in the prospective market.

Export restrictions on products – Apart from direct product export restrictions in field of technology and agriculture, a number of countries also have capital controls. Mca Manipulations in case study “ Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms ” should look into these export restrictions policies.

Foreign Direct Investment Policies – Government policies favors local companies over international policies, Mca Manipulations in case study “ Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms ” should understand in minute details regarding the Foreign Direct Investment policies of the prospective market.

Taxation & Regulation Impact on Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms

Corporate Taxes – The rate of taxes is often used by governments to lure foreign direct investments or increase domestic investment in a certain sector. Corporate taxation can be divided into two categories – taxes on profits and taxes on operations. Taxes on profits number is important for companies that already have a sustainable business model, while taxes on operations is far more significant for companies that are looking to set up new plants or operations.

Tariffs – Chekout how much tariffs the firm needs to pay in the “ Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms ” case study. The level of tariffs will determine the viability of the business model that the firm is contemplating. If the tariffs are high then it will be extremely difficult to compete with the local competitors. But if the tariffs are between 5-10% then Mca Manipulations can compete against other competitors.

Government Scheme & Subsidies Impact on Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms

Research and Development Subsidies and Policies – Governments often provide tax breaks and other incentives for companies to innovate in various sectors of priority. Managers at Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms case study have to assess whether their business can benefit from such government assistance and subsidies.

Consumer protection – Different countries have different consumer protection laws. Managers need to clarify not only the consumer protection laws in advance but also legal implications if the firm fails to meet any of them.

Political System & Stability, and its Impact on Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms

Political System and Its Implications – Different political systems have different approach to free market and entrepreneurship. Managers need to assess these factors even before entering the market.

Freedom of Press is critical for fair trade and transparency. Countries where freedom of press is not prevalent there are high chances of both political and commercial corruption.

Corruption level – Mca Manipulations needs to assess the level of corruptions both at the official level and at the market level, even before entering a new market. To tackle the menace of corruption – a firm should have a clear SOP that provides managers at each level what to do when they encounter instances of either systematic corruption or bureaucrats looking to take bribes from the firm.

Independence of judiciary – It is critical for fair business practices. If a country doesn’t have independent judiciary then there is no point entry into such a country for business.

Government attitude towards trade unions – Different political systems and government have different attitude towards trade unions and collective bargaining. The firm needs to assess – its comfort dealing with the unions and regulations regarding unions in a given market or industry. If both are on the same page then it makes sense to enter, otherwise it doesn’t.

Economic Factors that Impact Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms

Social Factors that Impact Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms

Technological Factors that Impact Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms

Environmental Factors that Impact Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms

Legal Factors that Impact Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms

Step 3 – Industry Specific Analysis

What is Porter Five Forces Analysis

PESTEL stands for political, economic, social, technological, environmental and legal factors that impact the external environment of firm in Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms case study. PESTEL analysis of " Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms" can help us understand why the organization is performing badly, what are the factors in the external environment that are impacting the performance of the organization, and how the organization can either manage or mitigate the impact of these external factors.

Step 4 – SWOT Analysis / Internal Environment Analysis

Step 5 – Porter Value Chain / VRIO / VRIN Analysis

Step 6 – Evaluating Alternatives & Recommendations

Step 7 – Basis for Recommendations

References :: Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms case study solution