Introduction to Negotiation Strategy
At Oak Spring University, we provide corporate level professional Negotiation Strategy and other business case study solution. Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms case study is a Harvard Business School (HBR) case study written by Dinesh Kumar Unnikrishnan, Tousif Ahmed Inayath Syed, Suresh Ganeshan. The Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms (referred as “Mca Manipulations” from here on) case study provides evaluation & decision scenario in field of Finance & Accounting. It also touches upon business topics such as - negotiation strategy, negotiation framework, Analytics, Ethics, Regulation.
Negotiation strategy solution for case study Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms ” provides a comprehensive framework to analyse all issues at hand and reach a unambiguous negotiated agreement. At Oak Spring University, we provide comprehensive negotiation strategies that have proven their worth both in the academic sphere and corporate world.
What’s my BATNA (Best Alternative To a Negotiated Agreement) – my walkaway option if the deal fails?
What are my most important interests, in ranked order?
What is the other side’s BATNA, and what are his interests?
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.
By interests, we do not mean the preconceived demands or positions that you or the other party may have, but rather the underlying needs, aims, fears, and concerns that shape what you want. Negotiation is more than getting what you want. It is not winning at all cost. Number of times Win-Win is better option that outright winning or getting what you want.
Options are the solutions you generate that could meet your and your counterpart’s interests . Often people come to negotiations with very fixed ideas and things they want to achieve. This strategy leaves unexplored options which might be even better than the one that one party wanted to achieve. So always try to provide as many options as possible during the negotiation process. The best outcome should be out of many options rather than few options.
When soft bargainers meet hard bargainers there is always the danger of soft bargainers ceding more than what is necessary. To avoid this scenario you should always focus on legitimate standards or expectations. Standards are often external and objective measures to assess the fairness such as rules and regulations, financial values & resources , market prices etc. If the negotiated agreement is going beyond the industry norms or established standards of fairness then it is prudent to get out of the negotiation.
Every negotiators going into the negotiations should always work out the “what if” scenario. The negotiating parties in the “Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms” has three to four plausible scenarios. The negotiating protagonist needs to have clear idea of – what will happen if the negotiations fail. To put it in the negotiating literature – BATNA - Best Alternative to a Negotiated Agreement. If the negotiated agreement is not better than BATNA then there is no point in accepting the negotiated solution.
One of the biggest problems in implementing the negotiated agreements in corporate world is – the ambiguity in the negotiated agreement. Sometimes the negotiated agreements are not realistic or various parties interpret the outcomes based on their understanding of the situation. It is critical to do negotiations as water tight as possible so that there is less scope for ambiguity.
Many negotiators make the mistake of focusing only on the substance of the negotiation (interests, options, standards, and so on). How you communicate about that substance, however, can make all the difference. The language you use and the way that you build understanding, jointly solve problems, and together determine the process of the negotiation with your counterpart make your negotiation more efficient, yield clear agreements that each party understands, and help you build better relationships.
Another critical factor in the success of your negotiation is how you manage your relationship with your counterpart. According to “Dinesh Kumar Unnikrishnan, Tousif Ahmed Inayath Syed, Suresh Ganeshan”, the protagonist may want to establish a new connection or repair a damaged one; in any case, you want to build a strong working relationship built on mutual respect, well-established trust, and a side-by-side problem- solving approach.
According to
Harvard Business Review
, there are three types of negotiators – Hard Bargainers, Soft Bargainers, and Principled Bargainers.
Hard Bargainers – These people see negotiations as an activity that they need to win. They are less focused less on the real objectives of the negotiations but more on winning. In the “Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms ”, do you think a hard bargaining strategy will deliver desired results? Hard bargainers are easy to negotiate with as they often have a very
predictable strategy
Soft Bargainers – These people are focused on relationship rather than hard outcomes of the negotiations. It doesn’t mean they are pushovers. These negotiators often scribe to long term relationship rather than immediate bargain.
Principled Bargainers – As explained in the seven elemental tools of negotiations above, these negotiators are more concern about the standards and norms of fairness. They often have inclusive approach to negotiations and like to work on numerous solutions that can improve the BATNA of both parties.
Open lines of communication between parties in the case study “Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms” can make for an effective negotiation strategy and will make it easier to negotiate with this party the next time as well.
Dinesh Kumar Unnikrishnan, Tousif Ahmed Inayath Syed, Suresh Ganeshan (2018), "Predicting Earnings Manipulation by Indian Firms Using Machine Learning Algorithms Harvard Business Review Case Study. Published by HBR Publications.
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