Compliance

AI is changing the Risk Management & Compliance

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Authored By...

Piyush Srivastava (RiskCounts)

In this increasing debate over AI, in how it is good for mankind, and on the other side, how it is going to take away our jobs, what is the right answer?

I would take the analogy of the advent of cars, trains or airplanes, in the times when people used horse carriages, horses or bullock carts. What was the advantage of a motor car or the train? Well, it reduced the time of travel, brought efficiency in terms of speed, reliability and saved valuable hours. So did the creation of an airplane, which made traveling across the globe a viable and easier options for the masses. If we look at the AI and machine learning today, it seems that it is no different from graduating from the horse driven carriage to a car.

Now, Artificial Intelligence (AI) is affecting multiple areas of our lives, like AI driven cars, back office automations, machines learning to operate non-stop operations and doing tasks in minutes, what would take a person perhaps a week to do the same manually.

Risk Management is no exception to this. Fintechs and Banks are introducing Artificial Intelligence (AI) applications in risk management in a limited way, but these applications are also finding usage in the areas of investment decision making that is supported by huge amounts of data, Hedge Funds and Asset Managers are using high speed trading using complex models. At the same time, phone based market making is giving way to electronic execution. Market Makers and Asset Managers are now looking to use technology and Artificial Intelligence (AI) to assess the risk of the counterparties from the publicly and privately available data. (Read More...)

 

Artificial intelligence (AI) services are transforming banking

photo credit: iStock

photo credit: iStock

Authored By

Jonathan Brawl (Mobile Business Insights)

Artificial intelligence (AI) is entering the mainstream at different paces for different industries. The insurance industry has so far outpaced the banking and asset management industries in terms of how frequently they use AI services to make important business decisions. While 54 percent of insurance companies are already using AI for these purposes, only 34 percent of banking institutions are doing the same.

But banking’s adoption of AI is projected to grow rapidly over the next few years. According to a survey from Narrative Science, 32 percent of financial institutions are using AI technologies for multiple banking purposes, and more than half of non-adopters plan to embrace AI by the end of 2018.

The applications of AI in banking will be varied and transformative for the industry. Here are a few of the most common ways consumers will see banks using artificial intelligence in the near future:

Real-time fraud detection

When it comes to detecting fraud, banks need to be able to identify patterns and behaviors that likely signal fraudulent behavior. Sophisticated criminals have tactics for covering their tracks and slowing down detection until they’re able to make a clean getaway, but AI services are much more effective at detecting patterns and fraudulent behavior.

According to The Financial Brand, AI is capable of detecting fraud in real-time, and it can even predict how that fraudulent behavior will progress over time, allowing the bank to stop the fraud and track down the perpetrator. Faster response times will also deter future attempts, lowering costs for the bank and protecting its financial assets.

Chatbots for customer service

Consumers are familiar with chatbots as a customer service solution, and they are already being used by businesses in other sectors. Banks plan to use AI-powered chatbots to provide fast, responsive service that more effectively serves the needs and expectations of customers.

With an AI-powered chatbot, banks can process and resolve customer queries and complaints faster, and less time spent on hold will be required to process requests. Business Insider reports that these chatbots will be used to assist in wealth management services, loan underwriting, customer analytics, fraud detection and other critical banking services. (Read More...)

How Artificial Intelligence Can Influence Governance, Risk, and Compliance

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Authored By

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Artificial Intelligence (AI) offers enormous opportunities to businesses. Given the correlation between risk and an organization's objectives, one could easily extrapolate how AI can help bring insight to Governance, Risk, and Compliance (GRC) activities as well.

But, what is AI? As a working definition, AI is the science and engineering of making intelligent machines and computer programs to achieve a goal. It's about creating a computer mind that can think like a human. It's about machines taking action.

One of the most important technological advances of our time is artificial intelligence, and, in particular, machine learning, which is the ability for a machine to keep improving its performance without human involvement to accomplish tasks. Systems can now be taught to perform activities on their own.

The transformative effects of AI will be felt across nearly all industries. The impact on core processes and business models will be enormous, placing further strain on management and implementation.

There are similar implications for risk management. Probably one of the best cases is fraud detection. Algorithms can be written using various stochastic modeling techniques, coding, and data testing. Of course, for machine learning to be successful, it must have quality data. As a result, there is a premium on structuring risk data in such a way to use it as AI input. Conversely, a challenge implicit in machine learning is substantiating its outcomes. As machines "learn," their conclusions may not always yield the desired result. This conceivably makes it difficult for a risk manager to explain the machine's conclusions to executives or a regulator difficult. For example, there may be issues with multicollinearity, lack of data, as well as how the machine deals with outliers, which is common with many risk data, especially if the organization uses external data. (Read More...)