Finance Made Easy with Artificial Intelligence and Machine Learning 

Finance with Artificial Intelligence and Machine Learning

Finances: A. I. M. Learning

Finance deals in facts and numbers. Both generate a huge amount of data, information, and records. This humongous amount, along with complicated calculations, complex decision making, and timebound result expectations, increases the difficulty in dealing with different processes in finance.

Though computerization and digitalization have taken this responsibility and become a fundamental support base for the modern finance market, the real transformation is now being brought by Artificial Intelligence (AI) and Machine Learning (ML).

This is the era of rapid technological progress, with new disruptive breakthroughs coming in daily. Every time companies cannot quickly absorb the sudden changes brought in by such technologies. That is where Artificial Intelligence and Machine Learning are coming in handy.

They were, until recently, being mostly used by hedge fund managers. But last few years are witnessing their spread across banking, insurance, security, equity, trade, regulatory, and fintech sectors.

All these sectors have been positively impacted by Artificial Intelligence and Machine Learning  where not only processes of underwriting, composition, optimization, model validation, market impact analysis, and autoboot-advising are speeding up but also such cases of mundane, time-consuming, and repetitive processes like alternative credit and risk reporting, account maintaining, and book-keeping are being performed easily, quickly, and accurately.

With improved hardware, software, and automated machine algorithms, AI and ML are personalizing, standardizing, and streamlining the different business processes.

Fintech: A. I. M. Loaded

ML is an application of AI, which is an extension of computer science. Artificial Intelligence makes systems automated so that they can learn from data automatically, and experience without being explicitly programmed can improve their processes. While ML generates, stores, manages, and analyses the data to draw meaningful insights. An AI and ML-loaded fintech service has many features which make it easy for financial service providers to enrich their consumer’s experience.

#Easy and Quick Solutions: AI and ML are faster, less time-consuming processes than manual operations because they can update and upgrade the process modules remotely and in real-time. And if an automated decision-making system is integrated, then it can make a prediction on millions of users’ behavior in seconds.

#Cost-Effective: Thus, it is a cost-saver, as the manual, people-based predictive models are too expensive to manage. AI and ML-based predictive models are replacing or complementing human capabilities. Also, the initial investment and even long-term maintenance costs are minimal compared to recruiting and training experts.

#Less Biased: As compared to humans, AI and ML are less biased. But to improve objectiveness and fairness, businesses must stay up to date with the challenges of combining AI, ML, and human intelligence to reduce systematic bias.

#Scalability: In Finance, micro-segmentation is a necessity. It improves customer-specific targeting, thus enhancing the probability of conversion rates. This leads to personalized customer interaction and customization of processes accordingly. And this is where AI and ML show their usefulness. As compared to manual systems, they can handle large sets of micro-segments easily and effectively, increasing the reach and scope of the service.

#More Customer Engagement: A better-interacted customer can be engaged in the business process more effectively, lessening customer dissatisfaction and customer loss. Further, by integrating predictive analysis and real-time decision-making tools into the CRM, the Product/Service recommendation engine can be applied to provide behavior-based suggestions, helping the user to make the right choice and fully utilize the service/product.

#Fraud Detection and Prevention:  Business and Customer security and financial safety are the most vulnerable areas to the increasing threats of global frauds, which are occurring faster, hardly giving any time to the institutions to respond to.

AI and ML algorithms can dramatically reduce the volume of fraudulent cases, number of false alarms and categorize the cases into a pattern of most, medium, and less severe ones so that experts can review the most severe cases properly.

#Optimum Credit Risk Evaluation: An Artificial Intelligence and Machine Learning-based predictive algorithm can do an immediate credit risk assessment of large numbers of customers. This allows CRM representatives to offer customized and relevant products/services. Thus, by expediting the overall credit risk evaluation process, the provider can better service their targeted customers.

Tech Support: A. I. M. Long-term

A survey of 400 plus businesses, by the Economist Intelligence Unit, in key global markets reveals that artificial intelligence has been adopted by 27% of the responding companies, while 46% of them are piloting at least one AI project. And as many as 70% of the respondents are leveraging ML for fraud detection, prevention, and prediction of cash flow events.

AI and ML in banking have positively influenced individual customer experiences around the globe, with a dramatic drop in physical visits to bank offices in the last two years. And according to Business Insider Intelligence’s ‘Mobile Banking Competitive Edge Study,’ as many as 89% of the customers instead prefer to use mobile banking apps.

The Mordor Intelligence report 2019 has evaluated the Artificial Intelligence and Machine Learning-driven finance market at $6.67 billion; and predicted that by 2025, it would reach to $22.6 billion level. Business Insider further reports that $447 billion (in just two years) will be the potential savings for Banks because of AI and ML applications.

• Gaurav Wankhade.

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