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Bespoke Financial Advice & Personalized Product Recommendations to Customers Using AI

Like many other technological advancements, artificial intelligence came into our lives with the prospect of holding unlimited potential in terms of transforming our personal as well as professional lives. Today, around 70 years from the day when the very term artificial intelligence came into existence, it’s become an integral part of the most demanding and fast-paced industries. Amongst those industries is the banking and the overall financial services industry, with numerous financial services providers actively exploring new AI uses in finance and other areas to get a competitive edge in the market.

The rise of AI in the financial services industry shows how quickly it’s changing the business landscape even in complex and traditionally conservative segments such as credit decisions, risk management, fraud prevention, personalized banking and more. One of the segments of the banking and financial services industry where the use of AI has grown in recent years is offering bespoke advice and personalized product recommendations to customers, thus helping them do more with their money. Although the use of AI has grown in this segment, it’s still well below its real potential. To put it simply, product recommendations and bespoke advice to customers through AI has the potential to fundamentally transform the whole industry. Along with increasing productivity and efficiency, it also has the potential of helping customers get considerable returns on their investment.

Let’s have a look at the benefits which the usage of AI in this field can provide, along with how to effectively implement it.

Utilizing AI for Product Recommendations

Recommendation engines are fast becoming a key aspect of artificial intelligence in banking and the overall financial services sector. Recommendation engines typically take into account the user’s past data and/ or various offerings from the bank like credit card plans, investment strategies, funds, etc. to make the most appropriate financial recommendation, thus enabling the user to do more with his/her money. AI powered recommendation engines have been very successful in recent years and have become an important part of revenue growth accomplished by some major banks in recent times.

A notable example is JPMorgan, which has been focusing massively on technology as it looks to cut costs and increase efficiency. Around a couple of years ago, JPMorgan launched a predictive recommendation engine to identify those clients who should issue or sell equity. The predictive recommendation engine had major success and afterwards, JPMorgan extended its usage in other areas of its business.

How AI Based Product Recommendations Help Customers in Making Smart Investment Decisions

  • AI-based product recommendations help customers in finding products and services that are undiscovered, relevant and immediately useful.
  • Moreover, product recommendation engines also enable customers to look for products outside of their traditional preferences, i.e.- a customer who always invests in equities can also be recommended a commodity to look into and if deemed good enough, to invest into.
  • As a domain, trading and investments depend on the ability to predict the future accurately. Machines are great at this because they can crunch a huge amount of data in a short while. Moreover, machines can also be taught to observe patterns in past data and predict how these patterns might repeat in the future. All of this means better risk assessment of the customer’s investment.
  • AI based product recommendations can accumulate data from the customer’s web footprint and can also create a spending graph, thus enabling customers to see their investment and the consequent returns, ensuring they make smart investment decisions.

How Can AI Powered Personalized Product Recommendations Help Financial Service Providers?

  • Higher Conversion Rates – AI helps in offering effective personalized recommendations to a customer, increasing the probability of the customer acting on it. Research has shown that users who act on recommendations are more likely to buy something.
  • Effective Identification of Product(s) for Every Customer – Through utilizing aspects such as user’s past buying and selling history and more, AI based product recommendation engines can direct products towards customers who are most likely to be interested in them.
  • Increase in Average Order Value – The average order value of customers engaging with product recommendations tends to be higher.
  • Better Customer Experience – With personalized and relevant recommendation at the right moments, there will be a marked increase in the overall customer experience.

Some Case Studies

  • A Los Angeles based company has built an AI powered product recommendation engine which is helping customers in digital wealth management. As for impact, customer losses on investments have reduced by around 25 percent.
  • Through application of product recommendation engines, numerable trading companies across the world are helping customers to spot new signals on price movement and make more effective and rapid trading decisions.
  • Some banks are using AI powered product recommendation engines for providing hyper-personalized and contextual help/advice/suggestions as well as recommendations/offers from the banks.
  • A Cambridge, Massachusetts based company is providing AI powered analytical solutions that provide advice/recommendation on complex financial issues. According to a Forbes article, traders with access to the AI-powered solutions in the days following Brexit used the information to quickly predict an extended drop in the British pound.

Challenges Before Implementation

While the potential benefits of AI powered product recommendations are immense, another fact that has to be accepted is that some organizations do struggle with structuring their approach to harness its power. There is a lack of knowledge and understanding of its potential use, experimentation, and evolvement on a wider scale. While still in the early stages of development, organizations face significant challenges in implementing AI technology. Some of those challenges are,

  • Technology Infrastructure – AI-supported activities such as bespoke product recommendations to customers require ingestion of large amounts of data, requiring the technology infrastructure to be agile and scalable. Here is where a lot of organizations struggle.
  • Data Issues – A lot of institutions are challenged by data issues such as fragmented data, lack of digitization and integration, data quality issues and more.
  • Budgetary Constraints – Some organizations also suffer due to the lack of wherewithal with regards to resources, time etc. required to deploy AI based activities effectively.
  • Lack of Knowledge – Limited understanding of AI concepts, lack of well-trained professionals who can build and direct AI supported products and services poses another serious challenge to AI adoption.

How Can Banks and Financial Services Companies Effectively Implement AI Product Recommendation Engines

As the above-mentioned points signify, it can be difficult for banks and other companies in the financial services industry to implement AI powered recommendation engines, but there are ways to overcome the challenges.

  • Banks and financial service companies should be ambitious and patient when it comes to AI implementations. Don’t think of AI as a plug and play technology with immediate returns.
  • Focus on cross functional skills and perspectives, ensuring business and operational people work side by side with analytics experts.
  • Focus on transitioning the company culture from experience based, leader driven decision making to data driven decision making.
  • Focus on Upgrading the technology infrastructure and knowledge skills of the workforce.
  • Concentrate on optimizing scalability for the AI powered product recommendation engine.

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