Artificial Intelligence solution framework for investment advice

Investment banks are evaluating possible avenues for efficiency improvement either through artificial intelligence (AI) or through automation. In fact Robo Advisors are disrupting the traditional wealth management model by attracting large number of millennial customers by providing a differentiated Investment management experience. At the same time new regulations are being introduced to ensure safety of investor funds from improper selling of financial products to gullible investors. There have been a number of instances where financial intermediaries had to pay huge penalties for not being able to appreciate the risks and bring in the right product features while selling the products to customers.

The topic of reviewing and selecting a product for the client is largely the judgement of an Advisor. Currently, advisors do not have a software to do a scientific and elaborate assessment of the suitability of a particular product. The attempt here is to provide a framework for an advisor to appreciate the risks in financial products through a comprehensive risk rating model.  The model also provides a capability to evaluate customer risk profile along well-defined risk parameters. The solution framework essentially matches the customer risk score with the product risk score to indicate suitability of a product.

Following are the key components of this solution framework:

  • Product risk scoring model
  • Customer risk scoring model
  • Recommender engine
  • Product risk scoring guidelines
  • Customer risk scoring guidelines

Solution Highlights

It is necessary to keep all the product data on the solution updated alongwith the risk scores. Product scoring is done on the following factors:

  • Volatility
  • Liquidity
  • Credit
  • Complexity
  • Interest Rate Risk

Scoring on each of these parameters is done on a scale of 1 to 10, with 10 indicating the highest risk. The model provides indicative guidelines to help score the products appropriately.

Scoring Model

It is necessary to score the customer for his risk appetite to identify the products he could safely invest in. The model provides a set of parameters to determine customer risk and indicative guidelines to help score the customer risk:

  • Investment objective
  • Investment time horizon
  • Willingness to take risk
  • Investment profile
  • Customer age
  • Annual financial commitments
  • Financial literacy
  • Return expectations
  • Ability to decipher implications of product features on earnings
  • Tax status

The model suggests products suitable for a customer basis his risk profile and scoring. It also would help evaluate suitability of a specific product for a customer by matching the product scores with the customer scores.

Accelerators

The solution framework acknowledges possible errors in human judgement and possibilities of arbitrary scoring for both, the customer as well as the financial products. In order to facilitate rational and logical scoring of the products and the customer, we have developed broad guidelines to enable the user to visualize risk scores in a step by step manner. Using the guidelines it is possible to quickly bucket the score in to High, Medium and Low categories. Categorization makes it a lot easier to assign more specific score to each of the parameter. The framework provides separate set of guidelines to score products and customer.

Conclusion

With the help of the framework, the advisor can easily evaluate whether a particular product in contemplation is suitable to the customer or not. A number of products have complex structures with delicately defined terms and conditions, thereby making it difficult for the advisor to do a thorough review every time a product is being suggested for investment. A tool of this kind simplifies the process of investment advice and reduces the overhang of penalties for possible improper selling of financial products.

Sangamesh G Kuruwatti

Associate Director - Asset & Wealth Management Practice, Virtusa . Sangamesh is leading the Asset & Wealth Management Practice within Virtusa. He is a Chartered Accountant and comes with multi-faceted experience of 28+ years in Technology Solutions, Consulting, Accounting and Operations, Corporate Finance. Sangamesh's experience spans across Information Technology, Financial Services and Manufacturing. He has written research papers on topics like Dynamic Asset Allocation, Execution Management Systems (EMS) Vs Order Management Systems (OMS), and Emergence of Managed Accounts Platforms and such other related topics.

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