Emerging technologies are slowly but surely transforming various industries and helping them develop new capabilities. Artificial Intelligence (AI) may have been around for a while, but it is only in the last decade that the technology started transforming the financial industry to a large extent.
Having said that, there is a lot of merit in understanding how AI and big data influence the financial services industry. But first, let us start by learning about the emergence of AI and Big Data.
The Emergence of AI and Big Data
Artificial Intelligence has been an emerging technology for over a decade, and only now has it been utilized to empower important applications in various industries. Some of the core benefits of the technology are faster turnaround times, real-time data processing, intuitive interfaces, and predictive analyses – all of which are important use cases in the financial services industry.
Additionally, as the world shifted to the web and started producing large volumes of data, it was only a matter of time before big data emerged – a way to harness insights from those large data volumes.
6 Ways AI and Big Data Are Transforming the Finance Industry
If you are unsure about integrating AI and Big Data into your operations, you must consider hiring an AI consultant who can help you unlock your business’s potential. Having said that here are a few ways in which these technologies are transforming the finance industry:
#1 Managing Risks and Preventing Fraud
With the boom in the digital revolution comes a boost in the number of malicious offenders looking to exploit vulnerabilities in your system. When you operate in the financial services industry, the stakes are high, and fraud prevention is rightly at the top of your priority list.
AI and Machine Learning enable you to deploy measures that lessen financial risks and prevent fraud to a large extent. With these capabilities, your system is more equipped to analyze user behavior, patterns, and locations to detect red flags.
You can also feed historical data into these systems including fraudulent transactions so the system is more equipped to flag and prevent similar patterns. Such systems can also lessen the currently rising investigative workload in the financial services sector, and enable your fund support operations.
#2 Delivering Targeted Marketing Communication
A financial services company or bank like any other company has to market its products and services to attract customers and generate consistent revenue. When we talk about AI streamlining operations to boost efficiency, we must also talk about the role AI plays in marketing processes.
With AI-driven insights into customer preferences, you can deliver tailored marketing messages for customers in different stages of the marketing funnel. Such targeted marketing communication is more likely to drive conversions and help you maximize revenue.
#3 Informing Investment Decisions
As discussed earlier, AI and ML-based systems analyze historical data and come up with useful insights into market trends, changing customer demands, and customer behavior and preferences.
If a customer is seeking advice about their investment decisions, or if your company provides resources like an investment proposal template, having AI and ML empower your knowledge base is a great idea. Even if you have a portfolio management company, AI can help you provide tailored recommendations to customers based on their previous investments for a more rounded experience.
#4 Personalizing Services to Customers
With your historical and real-time data being fed into systems at all times, your AI-equipped processes can easily personalize and tailor your financial services to be more lucrative to customers.
Modern banks leverage big data analytics to understand and analyze consumer spending habits and behavior so they can offer tailored products, services, and advice to customers. What’s more, AI and ML can help banks and financial service providers offer highly personalized services and offers that are more likely to be purchased by customers.
#5 Automating Processes and Tasks
Automation is the world of this decade, with companies aiming to automate most of their processes and tasks to boost efficiency and productivity. After all, automated processes tend to bring down turnaround times, and scope of errors.
The most basic application of process automation in the financial services industry can be seen in the AI-powered chatbots on the company websites. These chatbots have been trained to respond to the most common queries that customers are likely to have, reducing the burden on the customer service personnel who can focus on the more complicated queries.
When implemented well, these AI-powered tools and features can boost customer satisfaction and user experience on your website. Moreover, AI can also automate other processes such as data entry, and compliance checks – tasks that don’t require manual expertise.
#6 Creating and Managing Portfolios
As you may be aware, creating a good financial portfolio requires investors to consider a combination of factors such as risk tolerance, customer preferences, and investment goals. With AI-driven processes and strategies, you can use algorithms to create portfolios by analyzing a combination of these factors for each customer.
Such AI-driven algorithms can also help portfolio managers optimize the investment portfolios of their customers to achieve their investment goals. Moreover, they can also use the algorithms to come up with customized investment strategies that are likely to be received well by customers.
Concluding Remarks
Artificial Intelligence and Big Data are mere technological approaches without the right use cases and applications utilizing them. Like many other industries, the financial services industry has benefited a lot from artificial intelligence and big data applications. Not only does it promise automation of redundant processes, but also more efficiency and productivity leading to more customer satisfaction.
The post How AI and Big Data Are Transforming the Finance Industry appeared first on Datafloq.
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