Compare VERINT NEXT IT Alme vs ANODOT AI Analytics in AI in Fintech Solutions

We used the Google cloud for our SaaS offering for the fintech market. Implementation was relatively quick, however migration from AWS was a very complex affair, which we completed in stages. After deployment on the GCP, our data scientists tried the Google Cloud AI solutions. The primary challenges are in importing data and in collaboration between various user groups. We currently have an in-house stack for testing AI that can consume data from the Google Cloud Platform, and the team continues to use that.

For someone like me, constantly looking to sort, categorize, and arrange items, Saleforce Einstein was a godsend. It is perfect for organizing tasks, teams, recipient lists, contact details, and so on. It makes life much easier. You can do all this and also communicate effectively with your team members. The only slight problem is that it can be a little confusing. And you may get messages stating errors and asking you for administrator access at times.

I was looking for a product that would help process large amounts of data and is easy to use. This is exactly what I got with Einstein Analytics. It is exceptionally easy to use and is pleasant to look at as well. It is accessible from anywhere at any time, which is not something that can be said for a lot of other products. One major drawback, though is that it is steeply priced, especially for small businesses. Also, sometimes updates and the terminology can be challenging to understand. Apart from that, it is a great product for everyone.

With the increasing number of regulations in the financial sector, the complexity of handling them has also increased. Banks and financial institutions, are struggling to not only keep up with the dynamic regulations but also to comply with them 100%. This is making them spend unnecessarily high amounts of money to tackle this issue. In such a scenario, it is IBM Watson Financial Services that comes to the rescue. This platform uses AI as well as ML to identify and present incidences spanning across financial crimes and risks and GRC (governance, risk, and compliance). It helps banks that have limited clarity on what to expect and what obligations to fulfil when it comes to important regulations pertaining to KYC, AML, and the like. IBM’s regtech developments help tackle this issue and assists these institutions in making sound decisions, in a cost-efficient manner. With the increasing volumes of data that is being processed through these organizations, it is highly likely that they are going to need AI to handle as many issues as it can, so that their executives have time to carry out more important tasks.

We tested IPSOFT Amelia and were impressed by the range and quality of its responses. The range of customization possible is tremendous, which leads to a very pertinent response in every customer engagement. It seems to be able to gauge sentiment and emotions in the discussion, which is also a big plus for such a technology. We tried it on various customer segments and overall response has been very positive.

A great cognitive tool to help you keep up, deal with, and comply with the ever-changing regulations in the financial sector. As a cognitive AI tool, it will guide you about important determiners such as customer behavior, patterns in their behavior, risk analysis, and help you manage your risks wrt regulatory compliance. This plays a major role in meeting our customer requirements, and providing them more personalized campaigns, without compromising on compliance.

NINA is one of the friendliest chatbots available out there. It helps my customers solve many different queries related to taxes, grants, duties, and royalties. It’s natural language understanding is great for forming appropriate, human-like responses to customer queries. As it runs round the clock, my customers can have their questions answered anytime. Their feature of data analysis makes it particularly useful, as the responses seem to emulate human responses.

Nina as a virtual assistant has helped us to better handle our customers’ queries. What first took its customer care executives a long time to find and present, Nina can do in a matter of seconds. Since more than half of our customers prefer online transactions, Nina is perfect since she solves queries regarding basic information quickly. This makes more time available to customer care personnel to handle more complex issues which AI cannot handle today.