Integration of AI and Advanced Analytics

Disruptive developments have had a transformative influence on the finance industry, such as artificial intelligence (AI) and advanced analytics. They are also changing the way businesses communicate and manage their enterprises with their customers. The introduction and rapid growth of these innovations have allowed businesses to develop their processes and activities.

While data analytics refers to drawing insights from raw data, advanced analytics help to collect previously untapped data sources to gain analytical insights, especially unstructured data, and intelligent edge data. Artificial intelligence, meanwhile, replicates behaviors that are normally correlated with human intelligence. These include guidance, logic, problem-solving, training, comprehension, and manipulation. Creative artwork, music, and more can also be generated by some recent AI iterations, such as generative AI. Their synergy will bring considerable creativity to many sectors, while these innovations sound diverse. Advanced analytics algorithms can deliver additional output over OT when powered by AI.

The World Economic Forum reports that the COVID-19 crisis presented an incentive for advanced analytics and AI-based techniques to improve business leaders’ decision-making as well.

In a study conducted on behalf of Intel by Forrester Consulting, 98% of respondents agree that analytics is key to driving business goals. However, less than 40 percent of workloads use advanced analytics or artificial intelligence to exploit them.

In the financial sector, advanced analytics and artificial intelligence are emerging favorites as they enable businesses to authenticate customers, enhance customer service, and reduce the cost of maintaining appropriate levels of risk of fraud, particularly on digital platforms. The pace of fraud attacks and threats also rises as finance firms race inch to disruption. The combination of these technologies helps to minimize certain risks before any real harm happens, thus raising compliance. This is done by risk management, detection of possible criminal activities, avoidance of fraudulent transactions, and more. Since AI-powered analytical algorithms are adept at identifying patterns and processing vast volumes of data, improving fraud detection rates is crucial. They will help clients to authenticate any financial services they can use and send warnings to the client if anything is wrong.

For brand marketers, this fraud detection capability is often helpful in identifying good campaigns and avoiding unnecessary spending. Boston Consulting Group noted that companies in consumer packaged goods (CPG) can raise more than 10% of their sales growth through better forecasting of predictive demand, specific local assortments, tailored consumer services and experiences, optimized marketing and promotion ROI, and quicker cycles of innovation, all through the said technologies.

While factors such as data silos, fear of losing out on the race to digital transformation and agility have influenced businesses to focus on data-driven insights, to remain competitive in the market, they must leverage advanced analytics and artificial intelligence.

(This article was originally published in Passionate in Analytics.)

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