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Chitkara School of Actuarial Science & Analytics

Actuarial science is the discipline that applies mathematical and statistical methods to assess risk in insurance, finance and other industries and professions. Actuaries will provide the statistical probability of a future event occurring (such as accidents or natural disasters), and advise managers on how to reduce any likely financial impact of adverse events.


Analytics is the process of obtaining an optimal and realistic decision based on existing data and analysis is the process of breaking a complex topic or substance into smaller parts to gain a better understanding of it. Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyse behavioural data and patterns, and techniques vary according to organizational requirements.

“Actuaries and Analytics experts use statistics, modelling, optimization, clustering, and market research in making decisions and judgment, to managing complex risks, optimizing product design and improving outcomes and value for consumers.”

  • To prepare working professionals to take on challenging roles in the promising Actuarial & Analytics Industry in the context of globalisation.
  • To train the students in the latest developments in the area of Actuarial and Analytics across various industry verticals
  • To equip them with a good mix of theoretical and cutting edge practical skills so they can be immediately effective and productive on the job.
Unique Features:
  • Industry projects during the program will give the practical knowledge to apply and understand various tools and techniques
  • Regular workshops by Industry experts and Industry Interaction
  • Students can do their Actuarial Certifications from various Actuarial bodies across the globe.
Learning Outcomes:

After successfully completion of the programs students will be able to:

  • Use economic analyses to form judgments about future inflation and interest rates
  • Use data relating to future liabilities to estimate payments that need to be met
  • Project and discount future cash- flow using assumptions
  • Calculate the contributions required to build up a fund over time to meet future liabilities
  • Monitor the progress of the accumulation of a fund
  • Advise on reinsurance and other risk transfer mechanisms
  • Analyse the variation between the actual and expected experience
  • Manage the variation in the progress of a fund to ensure that future liabilities are met
  • Handle data in a critical manner
  • Manage the build-up of assets to meet future liabilities
  • Apply statistical data analysis and other decision science techniques to optimally solve real world business problems
  • Contribute to decisions on investment policies aimed at meeting future liabilities.
  • Understand where and when the expertise of other professionals is needed
  • Operate within an environment that requires professionalism, scrutiny and
    transparency in the disclosure of information
  • Communicate the results of all his/her work.
  • Objectively analyse alternatives using quantitative techniques
  • Apply cutting edge tools and technologies to analyse Big Data
  • Develop appropriate algorithms using machine learning techniques to solve business problems
  • Be able to effectively communicate the results of the analysis using appropriate data visualisation techniques
  • Demonstrate the ability to work effectively in cross functional teams

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