Finance is a broad area that includes a variety of functions. The way finance teams work has changed dramatically in the last decade as a result of rapid technological advancements. RPA, or robotic process automation, is being used in everything from consumer banking to capital management.
RPA algorithms use machine learning techniques to provide improved customer service and increase the productivity of financial processes. Since AI technology is still in its infancy, incorporating some of these use cases has proven difficult.
Financial firms are increasingly relying on RPA to power more complex use cases, thanks to developments in machine learning techniques. Here are five situations in which AI plays a significant role in finance and where progress is being made.
Consumer banking has always been a focus for technical advancements. Market banking has progressed from ATMs to IVR phone banking systems to AI-powered digital assistants that can answer basic consumer questions.
Although their customers are using online banking sites, banks collect a lot of data. Digital assistants learn the meaning of consumer requests and suggest the best action directions based on their behavior and request background. AI assistants are often used in phone banking to answer basic questions, with more complex questions being guided to human agents.
The use of synthetic data has several consequences for the insurance industry. When claims are filed, insurance agencies are responsible for tracking and checking a broad range of properties. Satellite imagery or photos were taken from an aircraft or drone are used to verify these claims in some situations.
Manually analyzing these images and comparing them to previous conditions takes time, so insurance companies depend on AI applications to speed up the process. However, since the image preparation process is manual, training these algorithms on such data is time-consuming and error-prone (which can lead to compromised results.)
Companies can use synthetic data providers like OneView to recreate synthetic images depicting a wide range of scenarios and harm. OneView’s advanced data generation technology enables businesses to rapidly and cost-effectively produce massive synthetic datasets.
Most significantly, the automated data generation eliminates the need to prepare data for algorithm training because OneView’s platform generates data that is “ready for training,” meaning that it is completely annotated and optimized for specific sensors, whether satellite, aircraft, or drones.
Insurance companies may use the One view platform to add any new artifacts or geographical planes to their data and train their algorithms in a range of scenarios, including pattern tracking, shift measurement, and asset effect from adverse events. As a result, the final algorithm will be completely prepared to deal with any possible combination of real-world variables.
Perhaps the most fertile ground for AI to make a mark in financial operations is fraud detection. As money laundering techniques have become more sophisticated, banks have switched to artificial intelligence (AI) to introduce fraud detection frameworks.
In real-time, AI algorithms search billions of transactions and can detect fraudulent or unusual spending patterns. AI is used by companies like Paypal to protect consumer accounts and alert them to potentially fraudulent spending. Pattern detection algorithms notify bank enforcement officers of potential threshold breaches, which can be addressed or ignored.
Beyond investment mandates, asset managers have started to use AI to assess portfolio risk and exposure. In terms of risk, portfolios containing illiquid financial instruments are difficult to calculate. Volatility affects the prices of these commodities. Portfolio managers use artificial intelligence to construct complex pricing models and set risk limits for their portfolios. These pricing models consider a broad range of financial data when determining a fair price.
Since the origins of this data are diverse and constantly evolving, AI is critical in assisting businesses in pricing assets and managing client portfolios. Managers will run stress tests on their portfolios to see how much they’ll be worth in various scenarios.