Why is AI Important

It seems like Artificial Intelligence (AI) is everywhere and seemingly in every industry. More and more business use cases are turning into real results every day. It can be hard to know how, why, and when to incorporate AI. Many businesses are simply unaware of the potential impact that machine learning algorithms can have on your efficiency and effectiveness. 

That is where Google Cloud and AI partners step in. Leaders need guidance, direction, and most importantly proven results. The financial services sector is adopting Machine Learning (ML) at a fast pace, which is taking finance companies to the next level and allowing them to gain deep insights into their customers like never before! It can be both exciting and scary to navigate Cloud and AI implementation. 

Why choose Google Cloud

Google Cloud Platform is a cloud provider and it offers the infrastructure to store, and analyze your data at scale without hassle, allowing ML models to output results and give recommendations based on massive amounts of data within seconds. Google Cloud can improve your revenue opportunities, enhance your customer experience, increase your operational efficiency, and create a productive ecosystem that is scalable while enhancing decision models. Using Google Cloud Platform’s infrastructure allows for real-time model training evaluation prediction and monitoring like never before. 

Improving Customer Care with Intelligent Chatbots

One use case for AI in the Financial Services sector, highlighted at Google Cloud Next 2019, is improving customer experience with chatbots. Chatbots allow customers to use voice and text to do simple tasks like check their balance and create a budget. 

The customer’s data flow is changing with the introduction of third party vendors. Banking mobile applications are important but technology is evolving and moving into the era of chatbots. Chatbots are expected to trim business costs by more than 8 billion yearly by 2022!

Voice assistants allow call center agents to work efficiently to reduce the amount of time that customers are on the phone and on hold. Google Assistant uses Dialogflow Enterprise, which is a tool that does intent matching based on natural language processing and has automatic integration. Using Contact Center AI and Dialogflow, a chatbot could potentially pick up the phone, answer simple questions, and route calls to the correct agent to avoid the dreaded on-hold music with multiple call redirections. 

Using Google Cloud Platform you can run ML on that chatbot data to improve other aspects of the business like improve sentiment and increase the relevancy of FAQs. 

Predicting Loan Delinquency with Machine Learning

Let’s look at another example of AI in the Financial Services sector, loan delinquency prediction. Pluto7 data engineers, a Google Cloud Premier Partner for ML and AI worked with Google Cloud Engineers to predict credit risk and customer lifecycle value by creating accelerated ML-based solutions using Google Cloud Platform. They used a large data set with behavior pattern data that includes voluntary repayments, foreclosure alternatives, expenses, net sales, mortgage repayments and more. The ML model that they created predicts risk and provides recommendations based on those predictions in seconds! 

Some of the Technologies that Pluto7 and Google Cloud used are as follows:

  • Google Cloud Platform – infrastructure on which the ML model is built. 
  • Google Cloud Storage – an online file system to centralize data on GCP. 
  • Big Query – used for data exploration and analysis 
  • DataPrep – an intelligent data service tool that helps you to cleanse, transform and explore data which preps the data for ML 
  • DataLab – the development environment. 
  • Cloud Source Repositories – this pushes the code and does code versioning 
  • Google Cloud ML engine – hosts the different models 
  • Cloud Functions – connects the different services together in GCP 


Financial Model Projections with Google Cloud 

Financial model projections are very important to the people that produce them and the people that use them to make decisions. One company, RMI Insights, helps its customers to automate financial modeling and produce intelligence from Machine Learning based analysis. RMI Insights was looking for a solution to scale and increase performance to meet future customer needs. They partnered with Pluto7 and Google Cloud to solve that problem. By leveraging Kubernetes, they were able to tailor their modeling and data processing workflows to their customer’s needs. Pluto7 helped RMI Insights with the migration and implementation of the machine learning resources using Big Query and Cloud Natural Language. 

RMI Insights was able to increase the speed of their financial models from 40 hours to minutes with GCP. They reduced their cloud infrastructure spend by 60%, enabled customized workflows based on customer needs through Google Kubernetes Engine and improved the sensitive client financial information security.

Importance of Change Management

Let’s end with this thought. With new technology, comes resistance, and the need for proper change management.  

When implementing AI, it is important to understand not only the business value but also the technical components and how your team will be able to implement them. It is important to remember that change management can make or break your efforts to implement AI into any business unit in your organization. Getting educated and creating a digitization strategy is crucial to success. 

Are you trying to improve existing ecosystems or create new ones? Whether you are a credit union, a bank, an insurance company or an investment company, you will need expert advice and knowledge of Google Cloud’s technologies. Pluto7 helps financial services companies build, migrate, and deploy Machine Learning Solutions and Guide you through the whole AI journey with Google Cloud Platform.