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Rise of Machines: Artificial Intelligence and Machine Learning

With finance being one of the most critical functions of an enterprise, CFOs should understand and leverage AI and ML to provide real time insights, inform decision making and drive efficiency across the enterprise There is a subtle difference between AI and Machine Learning.

AI is a branch of computer science attempting to build machines capable of intelligent behavior, while machine learning can be defined as the science of getting computers to act without being explicitly programmed.

Deep learning, a further subset of machine learning gaining lot of prominence of late, imitates the workings of the human brain in processing data and creating patterns for use in decision making.

Most hedge funds and financial institutions do not openly disclose their AI approaches to trading, but it is believed that machine learning and deep learning plays an increasingly important role in calibrating trading decisions in real time.

While earlier or conventional financial fraud detection systems relied heavily on complex and exhaustive sets of rules, modern fraud detection goes beyond following a checklist of risk factors –

By using machine learning for fraud detection, systems can detect unique activities or behaviors and flag them for security teams Machine learning algorithms can effectively process and get trained on millions of examples of consumer data such as age, job and marital status, as well as financial lending or insurance results including whether an individual defaulted or paid back a loan on time.

Banks and financial institutions that provides such a swift querying and interactive experience might pick up customers from traditional banks that require people to log into a time -consuming online banking portal and do the digging themselves.

The stock market moves in response to numerous human-related factors, and the ability of AI and machine learning to process and understand their large data sets will one day be able to replicate and enhance human financial intuition by discovering new trends and telling signals.

In addition to glitch-detection applications like those currently being developed and used in fraud, future security measures might require facial recognition, voice recognition, or other biometric data, powered by AL and ML in the background.

But these approval workflows don’t consider the broader circumstances, like if the requester is new in role and might require more supervision, or whether previous request from this requester been rejected or approved.

Some next-generation applications powered by machine learning can significantly optimize the cash application process by continuously analyzing historic data such as pay patterns, behavior and clearing documents, and based on this information update matching principles to clear payments automatically.

CFOs should consider using innovation labs, ideation forums, and create skunk work project teams where developers can bring together a discrete data set that hasn’t been tested before and use machine learning to identify hidden patterns.

By using AI to learn more from its huge volume of patient data, they redesigned the health card application process over three months by using variance detection to find fraudulent activity.

By letting self-learning algorithms find patterns and solutions in data instead of following preprogrammed rules, transactional tasks can be completed exponentially faster and with fewer people.

The real value in AI and machine learning are about gaining control, identify pain areas and bringing improvement by using advanced AI-based business solution, to drive the business forward, which is one of the fundamental responsibilities of CFOs.

With the automation of transactional tasks, CFOs and their teams can focus on partnering with the business to analyze available data, identify new business opportunities, and provide strategic guidance.

CFOs must be adequately aware of the expected digital angles to help solidify the organization’s digital strategy so that when a business case is up for review, they are well informed and can make the right decisions.

DARPA Announces $2 Billion Campaign to Develop Next Wave of AI Technologies

Over its 60-year history, DARPA has played a leading role in the creation and advancement of artificial intelligence (AI) technologies that have produced game-changing capabilities for the Department of Defense.

The agency's funding of natural language understanding, problem solving, navigation and perception technologies has led to the creation of self-driving cars, personal assistants, and near-natural prosthetics, in addition to a myriad of critical and valuable military and commercial applications.

However, these second wave AI technologies are dependent on large amounts of high quality training data, do not adapt to changing conditions, offer limited performance guarantees, and are unable to provide users with explanations of their results.

DARPA is currently pursuing more than 20 programs that are exploring ways to advance the state-of-the-art in AI, pushing beyond second-wave machine learning techniques towards contextual reasoning capabilities.

In addition, more than 60 active programs are applying AI in some capacity, from agents collaborating to share electromagnetic spectrum bandwidth to detecting and patching cyber vulnerabilities.

Under AI Next, key areas to be explored may include automating critical DoD business processes, such as security clearance vetting in a week or accrediting software systems in one day for operational deployment;

Accordingly, AIE constitutes a series of high-risk, high payoff projects where researchers will work to establish the feasibility of new AI concepts within 18 months of award.

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