Future lies in predictive analysis
After attending the last class on business analytics today, I sat back and reflected on the various applications of the different analytical tools in the sphere of finance. Over the last few days we have been frantically searching and downloading papers on how these tools have been helpful in decision making in our respective spheres of specialization. It would be interesting to know what the future of financial analytics is and what would be the new opportunities one could capitalize on going forward.
In India, in the recent years, banks have been increasingly using data analytics to better manage their Credit card, housing, auto and personal loans as well as insurance portfolios.
However, the future lies in predictive analysis by which the financial services industry could benefit in front-end customer acquisition analytics, relationship management, pricing optimization, risk management, offer selection and also actuarial analysis for insurance.
Predictive analysis can be used at different levels with increasing levels of sophistication. At the basic level, traditional techniques of linear modelling, regression, rule based algorithms and decision trees are used. At the more complex levels, neural networks or machine learning are used. Latest techniques such as text analysis (analysis of notes taken for a customer call or simple tweets) or social network analysis (looking for patterns in the relationship between a customer and provider) are slowly finding their way into the financial industry.
These techniques can be combined into compound engines such as net lift modelling where two or more scenarios are analyzed simultaneously to trace the different possible outcomes and choose the right treatment for a given situation. Ensemble modelling is another new concept in which a suite of models are run and the final response comes from weighting of the individual models’ results. In this, the model-weighting can easily be refined based on the situation.
Financial institutions typically make their money through the difference or spread between what they earn on their assets and what they have to pay for their liabilities. They have to ensure that this spread is enough to cover their operating expenses while providing for the incidental credit or fraud losses.
This profit calculation is maximized through risk management and price optimization. Predictive analysis will thus, in the very near future, be a part of their daily routine as they will be constantly required to load new transaction or behaviour data, evaluate assumptions, calibrate models, rebalance among methodologies and reweight results in ensemble infrastructures.
Predictive analysis will thus be the next leap for data analytics in the financial services industry.
AUTHOR: Aniruddha Dasgupta