Here at Aitomation, our team is constantly doing market research. One of the areas where we feel that really Robotic Process Automation should be applied is Banks. Banks and Robotic Process Automation are the ideal fit. So we conducted a thorough market research and came up with the following problems and solutions.
Below are the listed problems and their proposed solution by Aitomation. The reason we are making this public is because there is very little or no research on this topic as of yet. So this perhaps can serving as guideline into future research.
Problem 1 : Compliance Departments
Compliance departments have to verify a large number of transactions to potentially identify insider trading. Develop a technology that would leverage machine learning to recognize patterns of fraudulent behavior and automatically flag potential insider trades.
If we have definition of what is classified as fraudulent behaviour and means of verifying that manually, then a system can be created that can recognize patterns and point that out.
Problem 2 : Fraudulent Claims
Fraudulent claims make up a large part of the cost of providing insurance. Insurers could work together to monitor patterns and behaviour across multiple product lines. Develop a technology solution that would gather, standardise, and analyse data to identify fraudulent behaviour. This tool could also integrate data across multiple product lines for a more complete analysis.
If the process is clear enough. If we have people who know how to get this done, we can work together with them and easily create the required solution.
Potentially could be packaged with Problem 1.
Problem 3 : Banks for Millennials
Millennials have a different approach to personal finance. They may be less likely to consider longer term investment products. Create an interactive digital ecosystem that would include various saving and investment vehicles, while leveraging on robo-advisor technology, that would allow insurers to offer advice when appropriate.
This is very interesting. Aitomation can potentially partner up with banks, we can offer Millennials suggestions based on the policies and offers given by the bank.
For e.g. the person would enter amount X that they have in total. It would go to our software, and the result would be how to utilize that money through the bank that we have partnered with. It can be investing in vehicles, property etc.
Problem 4 : Robo-advice
Robo-advice is trending as a new way to explore customer needs and deliver financial advice. Develop an automated and real-time investment advice engine that would include: • A robust goal-based investing and planning framework • Algorithm-driven asset allocation and portfolio rebalancing. This should be customised, tax efficient, and with minimal human intervention • Real-time product recommendations that are in response to goals, market events, and trading history • Timely alerts and recommendations
Problem 4 & Problem 3 can be combined into one since both would require the robo adviser that tells them how to best utilize the investment capital that they have. With minor change for different age groups.
Problem 5 : Account publicly available information
Pre-packaged solutions offered by financial institutions may not be easily customised and thus adapted to customer lifestyles. Financial institutions could be more transparent on what their offerings are and what other people in similar situations have opted for. Build a platform that takes into account publicly available information (e.g. Facebook, Twitter and meta data) to determine individual protection needs and suggest a tailored solution.
This might be troublesome as the certainty of the data is in question. Ofcourse we are still looking into potentials solutions for this.
Problem 6 : Analyse customer relationship data
In corporate banking, a company’s decision to select a bank is influenced by many factors. Over the course of the relationship, there can be tell-tale signs of customer dissatisfaction (e.g. declining transaction volumes, declining operating cash balances, etc.). Develop a solution that would enable banks to analyse customer relationship data to identify telltale signs of customer attrition so that they can improve and build lasting relationships
This is one of the most interesting things and can be done relatively easily. We can monitor almost all kinds of transactions of the customer, analyse trends over time to see when have the transactions decreased in number or amount. We can also try and get publicly available data of the company and see how the company is performing and if the transactions, amount & number both, reflect the change in overall company standing.
We would love to hear from banks and RPA companies both about this. Please contact us here.