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Artificial Intelligence and Finance Industry

Machines have gained a lot of prominence over the years due to their ability to be used in many industries to solve complex problems effectively and quickly. Contrary to what one might expect, the charges for using the Learning Machines are not so difficult to deal with. The most common examples of problem-solving with machine learning are image uploading to Facebook and spam detection by email providers.

Machine Learning can solve a large number of challenges in different domains by working with appropriate data sets. We will see some of the challenges that machine learning solves and how they enable businesses to use their data more.

Increase AI and reduce risk. It provides the following benefits:

• Reduce regulatory and ethical issues

• Configures AI for control and interaction

• AI quality assurance process

• Combining AI capabilities to deliver real business value

Artificial Intelligence is changing the procedures for dealing with finance. It is assisting the diligence to modernize and enhance progressions extending from credit decisions to commercial risk administration. Over time, traditional banking has been transformed within financial services due to great technological innovations.

Artificial intelligence is practically used in diverse sectors and domains. Financial organizations are dealing with artificial intelligence in an advanced manner. Artificial intelligence is leading the industry of finance with a storm. It is being applied to obtain the rewards of effort reduction, cost-cutting, and bringing in value additions.

The implementation of AI and ML in the Finance sector improves efficiency and provides customers with services of greater quality. The finance sector is an early adopter of Artificial Intelligence and Machine Learning when compared to other industries. In some banks, Artificial Intelligence is often applied in areas like writing, processing, or concealment. Kai-fu Lee, a CEO of funding agency Sinovation ventures, which makes a specialty of AI and intelligent products, predicts that traditional jobs in the bank like clerks and operational staff will be the first to be impacted by job cuts.

When business processes become digital, business intelligence will automate the process and AI can make better decisions. Automated customer support, client risk profile, buying/selling trade, wealth and investment management, compliance, security, and complicated banking centers are some of the areas in which AI will play a key role.

1. Customer Support

Customer support is that the most blatant place in which we will see the benefits of AI in fintech and finally we will then trade people. Interactions with customers will increase with the adoption of AI.

Chatbots and AI interfaces like Cleo, Eno work with clients and solve queries, offering major power to cut the customer service jobs. This will start with the Human-Computer Interaction where AI will be able to suggest answers or scripts. However, when there is a need for human interventions in situations where machines do not understand how to handle a call (handling an angry or a tough customer), humans will be the backup for AI.

AI will be used only in the areas where they are proven to be effective. This process will reduce the response time and also lower the operating cost. Over the period as AI keeps accumulating more and more data, it will learn greater nuances to perform like a human and even better at times.

Humans will have to better their soft skills like communication, empathy, and persuasion to stay relevant for a longer time. While customers appear to consider the prominence of chatbots, they’re unaware of the existence of a wide quantity of Natural Language Processing (NLP) tools in AI that can empower more human-like interactions.

2. Consumer Risk Profile – Fast and Reliable

There is a generic change in mindset amongst corporate banks that “you only get credit if you don’t want it”. A few years ago, a McKinsey report brought out the rising dangers of risk management in a digital world. There is a changing dynamic in the world of BFSI, from digitization, automation to sharing highly sensitive information with third parties has increased the implications of risk.

Keen human-eye can no longer effectively handle structural risks or detail the plans for future execution. To ensure seamless, cost-effective, and error-free risk management, technology solutions especially the ones powered by AI have become crucial.

AI has been a game-changer with its utilities such as chatbots, virtual assistants, robotic processing automation, where it has already streamlined the process of updating the customer information and KYC and provides predictive analytics for future banking trends.

AI and ML have got the deep learning techniques that can read the transactional behaviors and identify trends based on past activities. With advanced analytics, segmentation techniques, and credit risk modeling, AI-driven tools can rapidly identify fraud transactions. AI-augmented fraud prevention and Anti-Money Laundering (AML) solutions can identify and cut down false positives and investigation time for fraudulent transactions.

AI can also benefit in real-time transaction monitoring, blocking suspicious activities, robust risk management to reduce fraud and money laundering, better turnaround time via cybersecurity and automation processes. Effective detection of risks can improve a bank’s reputation among customers and result in increased business and revenue.

With unforeseen and complex challenges faced by financial institutions in acquiring customers due to inefficient risk management processes, AI and Machine Learning tools and techniques are the go-to solutions for de-risking the way forward.

3. Trading, Wealth, and Investment Management

People are more likely to accept economic guidance from a fellow human being than a smart machine. Natural Language Processing (NLP) software can be used by Trading and Wealth Management platforms to scour the web for news related to mergers and acquisition for example or they could do a sentiment analysis of certain companies based on the data mined from social media to analyze how consumers are reacting.

This will help trading and wealth management companies to make informed decisions on which stocks to buy or sell. The predictive analytics tool can essentially predict the stocks that can make yield high returns. Algorithms can run on several thousands of stocks and correlate the data points that result in higher returns.

Machine Learning algorithms can also monitor and rebalance the customers’ profiles automatically. It can provide investment advice for portfolios of ETFs by gaining an understanding of customer’s financial objectives and risk tolerances based on the questionnaires.

ML algorithms can monitor customer’s account profiles daily and identify tax-loss and re-balancing opportunities and then initiate buy or sell orders accordingly. The trading orders can be sent to human investment managers for review before executing the orders.

Asset Management AI applications like predictive maintenance are quite established with different use cases across sectors and manufacturers have been seen adopting the technologies. Investment management platforms have AI applications to predict the performance of hedge funds or in risk modeling.

Portfolio Management providers have been adopting AI applications in the form of Chatbots and ML algorithms. Robo advisors have been widely used for enhancing customer experience. Large investment firms will be taking advantage of the benefits of AI applications such as reduced time to connect with customers and increased customer association lifetime.

Artificial Intelligence is prominent in creating benefits unforeseen in the finance industry. AI utilization and adoption, in the long run, will bring huge improvements in the way business is done. The industry is probable to have a cost-cutting of 23% from the budgets when compared to traditional and outdated monetary institutions; this all along except for an additional $1 trillion by the 2030 year-end (Kraus & Palmer, 2018).

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