Artificial Intelligence is one of the most alarming and popular emerging financial technologies in the modern age. With the different enterprises spread across different industries, artificial intelligence is the common thread that can immerse their technological efforts and innovations to success. During the first episode of the new podcast series Future on a Platter, Anand Subramaniam, Principal Architect AI & ML Practice, Aspire Systems discusses with Ashish Cherian, BFS Research Analyst, Aspire Systems and answers a variety of questions on the different attributes of the AI and ML practice and its importance in the Banking industry.

Here is the transcript of the entire episode.

Ashish: Artificial Intelligence has been a term that has been used for quite some time. The term was coined in 1958. However with the latest advancements in technology, we have seen a huge rise in the use cases around the retail industry, with the other industries slowly catching up. The word AI has also been used in several discussion forums and debates and it has also permeated into several business industries and almost every firm wants it. Every firm is trying to implement AI. Now, most banks in the US have all implemented chatbots to reduce resource hours and they use man-force for higher quality jobs. The Financial Brand has come up with an expert calculation that states that there is a 1 trillion dollar opportunity for banks that use AI. A 1 trillion dollar opportunity is a 22% increase in cost savings. When you have a budget that is in millions, a 22% reduction in costs is massive.

What do you think is the major advancement in AI for banks and how do you think has been the progress so far?

Anand: The adoption of AI in the banking industry has been a slow, but steady process. I humbly believe that, banks have shown a great amount of verve in accepting the fact that AI has to be adopted to keep pace with customer expectations. The closest rival of banks, i.e. the retail domain has already been venturing into the AI platform and have greatly excelled. In the banking domain, there has been a bit of slowness in adopting AI into their system, mainly because of the way in which the banks have been historically built. Banks have been built on customers and their diversity. Another fact is that services and products of banks are ones which cannot be seen but can only be experienced. The data collected by banks is fragmented across different layers, such that banks are not able to effectively capture the data in one repository. This includes the mid-tier and small banks as well. These banks are still in the mode of account centric banking and not customer centric banking. Hence the data obtained is not as good as banks would like.

Ashish: The retail industry with big giants such as Amazon and Flipkart and the media and entertainment market with their giants such as Netflix have implemented AI and ML perfectly. Basically, they know what I want much more than what I know myself. How has the retail industry implemented AI and ML and excellent recommendation engines and intelligent search options? Also is it fair to compare the retail and the banking industry?

And what page can the banking industry take the retail industry success?

Anand: I would not just garner Amazon and Netflix but also brick and mortar organizations like Walmart who have implemented AI into their system. There is a constant effort by retail organizations to understand more about their customers. Their business thrives on this factor. The loyalty that a customer shows towards a retail organization is extremely volatile when compared to the loyalty that a customer shows towards a banking organization. For example, one bad experience won’t lead to closure of accounts at banks. However at retail organizations, customers don’t have that sort of patience. Hence retail organizations have no other choice but to adopt the major emerging technologies. The most important thing that banks have to realize now is that retail organizations are slowly moving into banking as well. Hence banks are also at a threat from the retail enterprises.

Ashish: Exactly! Initially companies like Netflix were having a rating based system with moderate accuracy but then after that they built a model based on collaborative filtering. It was something like a modern day miracle. There was even this report which showed that Netflix had divided their viewers into 2000 taste groups based on the different intricate details involved in a user-user collaborative filtering process.
Anyways regarding that, what can banks do and what should banks take up as the principle factor or aspect which can help them to drive their AI and ML agenda.

Also what do you think that banks can do to achieve true banking and true customer success?

Anand: The main aspect is to get your data right. If you don’t get your data right, your AI and ML processes are just projections of your data. Banks have to look at setting their data strategy and data maturity model in the right manner. When looking from a customer angle, it should be a move from an account centric approach to a customer centric one. This would help in integrating all the data into one place. After this is done, implementing AI and ML on top of the good customer data will help them to serve their customers better and also would help them to enrich their customer data, time and again.

Ashish: That is very much true. I feel that once the data ingestion phase is done, you need a proper data to make you don’t have any incorrect values within your data. Now, to move onto our rapid fire round:

On a scale of 1 to 10,how much have Banks achieved in implementing AI and how far are they from achieving the maximum potential for AI and ML in banks?

Anand: I would give it a rating between 4 to 5 out of 10.

Ashish: What are the most highly rated technological advancements that can happen for banks in 2019?

Anand: NLP, Conversational Banking would be the major growth points in 2019. Post 2019, banks would see a lot of blockchain being introduced.

Such conversations on AI and ML are highly important. Experts state that most enterprises have set up AI teams to produce insights. However due to lack of correct and proper data, enterprises struggle in terms of having an engaging customer experience. It is the need of the hour that a framework is chosen to ensure data is extracted, collected, cleansed and used to engage with the customers. With a look at the horizon, it can be safely stated that there would exist two types of companies in the future – one which implements AI and obtains customer success and another which is in its terminal stages.

Future on a Platter is a podcast series which focusses on the latest emerging technologies in the banking and fintech industry.

To check out on the latest episodes which are available on the platter, check our page here.

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