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5 Ways in Which AI is Revolutionizing Finance & Insurance

Finance is a diverse field that encompasses different functions. Rapid technological advances over the past decade have changed the way finance teams work. Everything from consumer banking to capital management uses RPA or robotic process automation these days.

RPA algorithms rely on machine learning techniques to deliver better solutions to customers and build more efficiency into finance processes. AI technology is still nascent, and implementing some of these use cases has been challenging.

However, thanks to advances in ML techniques, financial firms are increasingly relying on RPA to power more sophisticated use cases. Here are 5 scenarios where AI involvement is significant in finance and where progress is occurring.

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1.   Automation in Consumer Banking

Consumer banking has consistently been a target of technological improvements. From ATMs to IVR phone banking systems, consumer banking has now leaped to AI-powered digital assistants that can answer simple consumer queries.

Banks collect a ton of data while their consumers spend time on online banking platforms. Given their behavior and inquiry history, digital assistants learn the context of customer queries and recommend the best action paths. Phone banking also uses AI assistants to answer simple queries with more complicated ones directed to human agents.

A good example of this Bank of America’s AI system Erica which serves over 66 million customers. The next step for AI in consumer banking is to drive greater personalization and product recommendations. Consumers interact through varied communication channels such as messaging, online banking, phone, and email.

As a result, banks have tons of unstructured data that they can feed their ML algorithms. The future of consumer banking is an omnichannel presence with intelligent assistants anticipating consumer needs and delivering tailored financial products. Such environments will help banks enhance the value they derive from customers and serve them better.

2.   Insurance and Asset Monitoring

Synthetic data usage has many implications for the insurance industry. Insurance companies underwrite a wide variety of assets that need monitoring and verifying when claims are filed. In some cases, these claims are verified via satellite imagery or photographs taken from an aircraft or drone.

Analyzing these images manually and comparing them to previous conditions manually is a time-consuming task and insurance companies rely on AI applications to speed up the process. However, training these algorithms on such data is labor-intensive since the image preparation process is manual, and thus prolonged and error-prone (which can lead to compromised results.) Additionally, images depicting before-and-after damage are usually hard to come by. What’s more, the frequency at which companies encounter claims in real life varies.

Synthetic data providers such as OneView help companies recreate synthetic images depicting a wide variety of scenarios and damage. Thanks to OneView’s advanced data generation technology, its platform enables companies to generate large synthetic datasets quickly and cost effectively. Most importantly, the automated data generation relieves the need to prepare the data for algorithm training, as OneView’s platform creates the data “ready for training”, meaning, fully annotated customized for specific sensors, whether satellite, aircraft or drone.

Insurance companies can utilize the OneView platform to add any new objects or geographical planes to their data and train their algorithms in a wide variety of scenarios such as trend monitoring, measurement of change, and the impact of adverse events on assets. The result is that the resulting algorithm will be fully prepared to deal with any combination of real world factors that might occur.

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3.   Transaction Data Enrichment

AI’s most powerful use exists in its ability to enrich transaction data. Banks and financial institutions are privy to a large number of transactions carried out by their customers. These transactions are currently aggregated and subject to analytics.

However, current analytics only provide a snapshot of what happened. For example, personal finance apps are an example of how aggregated data helps consumers make better decisions. AI has the power to move beyond past data and to look ahead, and offer insight into potential problems down the road.

These insights hold immense potential for PE and VC firms by allowing them to identify growth opportunities in their portfolio companies and spot mergers that could enhance business value. Banks can benefit by acquiring insight into customer needs and tailor loans and other financial products better.

4.   Fraud Detection

Fraud detection is perhaps the most fertile ground for AI to make a mark in financial operations. As the sophistication of money laundering schemes has grown, banks have turned to AI to implement fraud detection frameworks.

AI algorithms scan billions of transactions in real-time and can spot fraudulent or abnormal spending patterns. Companies such as Paypal use AI to secure customer accounts and to alert them to possible fraudulent spending. Pattern detection algorithms alert bank compliance employees to threshold violations that can be acted upon or ignored.

Synthetic data plays an important role here as well since the nature of financial fraud keeps changing. As fraudulent schemes become more complex, companies can help their algorithms keep pace by quickly modeling large datasets and training them.

Read More: Addressing AI Bias in Online Identity Verification with 5 Critical Questions

5.   Portfolio Risk and Scenario Modeling

Asset managers have begun using AI to evaluate portfolio risk and exposure beyond investment mandates. Portfolios that hold illiquid financial instruments are tough to quantify in terms of risk. The prices of these assets vary based on volatility.

Portfolio managers use AI to create complex pricing models and determine acceptable risk limits for their portfolios. These pricing models take a wide variety of financial data into account and calculate a fair price.

Since the sources of these data are varied and ever-changing, AI is essential in helping companies price these assets and manage client portfolios. Managers can stress test their portfolios to figure out what they’ll be worth under different scenarios.

A Giant Leap Forward

As AI evolution gathers pace, we can expect financial operations to take a quantum leap forward. The 5 use cases highlighted in this article barely scratch the surface when looking at the potential AI has to revolutionize finance.

Read More: Are You Afraid of the Data?

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