Rakesh Kumar

Deployment Challenges & Best Practices of AI implementation in Financial Services

Blog Post created by Rakesh Kumar on Aug 22, 2018

Overview

Artificial Intelligence is expected to have profound impact on almost all the industries and Banking & Financial Services is no exception to it. Banks are trying to deploy a combination of AI technologies like machine learning and predictive analytics to provide personalized and contextual services to customers.

 

Deployment Challenges

Like any other new technology there are many issues companies face venturing into AI. On one hand, it’s technically difficult to efficiently handle large amounts of data, on the other hand there is the challenge of training the deep learning systems to work efficiently with lesser data and organizing them as per the requirements.

 

Source: NASSCOM-CMR AI for BFSI Report

 

Banks are governed by multiple rules and regulations. Deploying AI systems which can manage security and privacy as per accepted standards and regulations is a big challenge followed by its ability to interpret questions and provide correct answers that meet the set objectives. Then, there is the challenge related to customers, who are still more comfortable interacting with humans, it will take them some time to adjust with the new automated AI systems (chatbots, voicebots, etc.).

 

Best Practices

Increasing market volatility and growing size of available data make implementation of AI and cognitive technologies the top most imperative for Financial Service players. Every AI application will have its own advantage, but there are some common best practices to follow.

  •        Data Management strategy: It is important to organize the data in a structured manner which can be interpreted by all the AI system. For AI systems to run successfully it is important to have right tools that can extract the data and store them in a centralized place which can be accessed by all AI systems.
  •        Feeding historical data: For an AI system to start delivering results immediately, the system requires to be fed with historical data, which the AI systems can use to self-learn. This can be very useful while answering customer queries as it needs to interpret old conversations to support the answers.
  •        Post implementation testing: Just because an AI system delivers the right outcomes from historical data doesn’t mean it will do so post Go-live. It is important for an AI systems to function independently for its success, as it may behave differently with actual data as compared to historical data. Therefore continuous testing using multiple parameters is extremely important to ensure accurate results.
  •        Combining public and private cloud: Designing an in-house AI infrastructure would be more apt to align the workloads that can match the exact requirements. Another option is to run AI workloads completely on a public cloud that can tap the cognitive services of an existing public cloud services using vendor like AWS, Google, or Microsoft via APIs.

 

To conclude

Bankers should primarily use AI to reduce human errors and create a robust business process system which can deal with manpower intensive processes. With time AI should reach a stage where it can use deep learning technology to continuously improve itself through self-learning to help increase accuracy and predict outcomes.

 

To know more, download our latest research paper titled “AI for BFSI”: 

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