Presenter: Define what you see as the application of AI in Fintech for anyone that’s in the audience and is new to the industry.
Ronak: In the simplest of the terms, and in the context of FinTech, AI can be defined as follows:
- Machine Learning Level 1 – This is where we use the RPA robots to observe the patterns in users’ actions. This forms part of the process-discovery where the robot is installed on a human’s machine and is learning from their actions. This is how the machine learns how to mimic the user’s actions.
- Machine Learning Level 2 – This is where we feed a huge amount of relatively structured data and let the robot process that data, identify patterns and follow pre-defined scenarios and process flows. Once the robot learns the patterns it will forever continue following the same path, thus saving time and energy from the human input
- Machine Learning Level 3 – This is where a robot processes relatively unstructured data and identifies unknown patterns and scenarios that humans are usually not able to see without significant effort or plenty of experience. At this stage, we can truly say that the machine is learning from its own past experiences rather than learning from humans.
- Deep Learning Neural Network – Although there are plenty of theoretical examples, where the machine will be able to consciously make decisions, however, the practical uses of this technology are still in the works.
Presenter: What are you doing in your sectors with AI that specifically aims to reduce cost and increase the customer’s experience?
Ronak: We design and develop bespoke Robotic Process Automation bots that follow the same ML1, ML2 and ML3 described above for our clients mainly in the FinTech sector.
This includes bots for:
- Transactions Monitoring and AML
- Regulatory reporting and risk mitigation for FATCA and Tax
- Accounting and bank reconciliations
- Procurement and Ordering
- Customer onboarding, service and relations management
- Lending and debt processing
Presenter: What are the most prominent challenges in implementing AI?
Ronak: The challenges range from educating clients and the public about the benefits of AI, to more complex one like cleansing the data and choosing the right platform.
The main challenges also include:
- Lack of awareness and education
- Lack of Agile Solutions that the business owners can try as a proof of concept
- Stigma and worries around AI, for example, “will it break?”, “will it replace workers?”, “will we all be out of jobs?”
- Legacy systems and data storage
- Lack of structured data to feed to the bots
Presenter: How well regulated is this area and what type of regulation do you think is missing and why?
Ronak: Currently, the same set of regulations apply to Applications of AI in FinTech, as they apply for any processing within FinTech, may it be done by humans or by established software.
However, in recent years, we are seeing more structured regulations specifically designed for AI are coming up.
Some examples include:
- April 2021 – European Commission laying down “Harmonised rules on AI”
- In the UK there is currently no AI-specific legislation. This is because laws, by their very nature, have to be technology agnostic to ensure that future technology will still be subject to an overarching legal framework – Read an article by the KPMG here
- UK Govt published a report in Feb 2020, laying down recommendations on the “Impact of AI on Public Life”
Presenter: What would be your key takeaway and piece of advice when applying AI in Fintech?
Ronak: Businesses should proactively seek to implement and adopt robots with relatively safe technologies like Agile Automations and Robotic Process Automation. These carry a minimal amount of investment with high ROI, and we are keen to take on new projects to provide proof of concepts.
We want to showcase the amazing benefits and versatility of custom-coded RPA!
We are constantly looking for new business challenges and would love to build a proof of concept automation for you. This way you can benefit from fully supported RPA solutions without the need for massive investments or expensive changes to your systems.
