The concept of Artificial Intelligence (AI) has always been intriguing, be it the way it has been portrayed in movies or the way it is currently being put into use by various industries. From manufacturing to financial services, AI is gradually lending its amazing capabilities for faster product creation, better customer satisfaction and is reducing the workload to a great extent. In the posts earlier we briefly discussed how Artificial Intelligence and Machine Learning are being used in Financial Services and also about the 5 Ways AI Is Transforming the Financial Institutions. But that isn’t all, there are many more ways how Artificial Intelligence is helping Finance Industry. Here’s a comprehensive list of the most common popular ways in which how artificial intelligence is helping finance industry
The primary and prevalent way of how artificial intelligence is helping finance industry is in customer service. Right from utilizing data to deliver customized experiences to 24X7 assistance and to the more advanced predictive analysis, AI offers customers a holistic and personalized experience.
While Chatbots are the more popularly known application of AI, a new arena has been created with the introduction of digital assistants. The best aspect of AI-based digital assistants is that it constantly evolves by analyzing every conversation and incorporating dynamic changes accordingly.
As discussed in the previous posts too, fraud detection is one of the most common applications of AI in financial institutions, fraud prevention is an extended application of the same. How is it done? AI (Machine Learning) allows machines to learn from huge repositories of data, and this learning leads to analyzing patterns, behaviors, etc. It allows machines to analyze suspicious behavior inferring from past records and flagging such activity, hence preventing frauds.
Risk Assessment and Management is again an area where AI can bring in better results. One way of risk management using AI involves using it to combine policies, procedures, and controls with the regulators and regulatory changes, hence safeguarding compliance.
Another interesting and growing application of AI in the Financial sector is investment prediction. The scenario is similar wherein the computational power of computers and the methods learned in Artificial Intelligence are put to practice, in the stock market, insurance, and other investment options. The machine hence helps investors to make predictions on the future of stocks and other investments based on the predicted market changes.
With digitalization, the global security threats that financial institutions face have grown too. But data scientists have now created AI-based tools that learn from the existing data and recognize patterns, which is then used to identify cybersecurity threats in the networks, allowing the financial institutions to prevent security risks and avoid data leaks.
Loan Underwriting is an extensive and tedious process. In the current world of Big Data, there is so much data to sort from. AI-assisted loan underwriting utilizes big data, as well as social, business, and internet data. The utilization of unstructured data provides a 360-degree view of the applicant, hence greatly improving the quality of underwriting.
If reports are to be believed, data-based learning is touted to be the future of Algorithm trading. Financial institutions are now increasingly utilizing algorithms created by AI to make effective decisions and successful tradeoffs.
Robotic Process Automation (RPA) has been gaining popularity in various industries including the financial sector. AI is now slowly taking over the mundane and tedious paper-driven processes and manual tasks, henceforth increasing the efficiency and speed and allowing employees to shift their focus to more value-added roles.
Natural language processing (NLP), is an application of AI that has been increasingly popular since its introduction into the market. NLP based software is trained to recognize attributes that are important for extraction and summarization in the documents and hence in data interpretation. The attributes are determined by the Financial institutions.
An extension to the personalization feature offered by AI is content creation. Financial Institutions are now employing AI to make their digital content i.e. emails, advertising, social media, etc. more efficient and effective using the transactional and behavioral insights gathered daily.
Trade settlements comprise of labor extensive back-office processing, a task that is simplified with the help of bots. AI-based trade settlements are done through effective identification from historical data, analyzing anomalies and gaps in the settlement process, and prediction using pattern recognition analysis.
Fighting money laundering is an arduous task. The cost of fighting financial crime is not a joke either. Data scientists have made great breakthroughs in recent years, allowing Financial Institutions to put AI to good use in preventing money-laundering by analyzing huge amounts of raw data and identifying patterns that might signal money laundering.
AI with its advanced data analytics has become a formidable force in marketing and is now being put to use by financial institutions too. AI in marketing provides the option to automatically send, or push notifications, communications, etc. to the right people at the right time (identified through analytics) and do so without raising any compliance red flags.
Custom Machine Learning Solutions
Machine Learning is constantly evolving, and data inputs allow it to learn faster. Financial institutions are now even keen on creating AI- Machine Learning based learning solutions that are specific to their own database hence seamlessly integrated with the specific regulations, compliance requirements, etc.
The role of AI in the finance industry is still evolving. While Artificial Intelligence is helping the Finance Industry greatly, with a better quality of human inputs and increased data to sort and learn from, it can be more proactively assistive. The Anti-Fraud Technology Benchmarking Report by SAS and ACFE indicates that “55% organizations are keen on increasing their budget for AI and machine learning technology to fight fraud, followed by predictive analytics and modeling over the next two years.” While this may be just one instance, yet it clearly indicates the increasing popularity of AI. And unlike popular misconception, AI isn’t a threat in terms of job replacement. Instead, it has helped in improving the quality of work and also created new jobs in many cases.
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