How can industries leverage Machine Learning

In this article I explore use cases in different industries - Healthcare, Retail and Financial Services and opportunities to leverage Artificial Intelligence.

A) #HEALTHCARE AI

1. IoT sensors and portable diagnostics:

  • Wearable devices & sensors are used to assess patient's health. Digital health platforms continually monitor vital signs using sensors (worn on the body) info is sent to a ML analytics center to flag anomalies.


2. Image analysis:

  • Machine learning systems are being tested to improve medical image reconstruction, noise reduction, quality assurance, segmentation, computer-aided detection, computer-aided classification & radio genomics.

3. Robotic surgery:

  • Robots perform procedures for orthopedics, urology, general surgery, gynecology, neurology, thoracic, otolaryngology, bariatric, rectal & colon, oncology, dental implants & hair transplants.

B) #FINANCIAL SERVICES AI


1. Fraud detection:

  • Fraud & anomaly detection - using geolocations, IP addresses, differences in billing & shipping addresses, purchase amounts versus regular activity.
  • Leveraging AI algos results in more accurate predictions & improved customer service.


2. Credit decisions:

  • Affordability scoring considers financial data - income, banking history, tax payments, however inclusivity is lacking.
  • Newer AI systems ++alternative data like social media, geolocation data, and other smartphone info & behavioral traits for patterns & insight (consent to data usage is key).

3. Robo-advisors:

  • Can provide automated financial guidance, portfolio management & servicing advice using algos & stats to manage investment portfolios.

4. Insurance & Robo-advisors:

  • Deliver services from handling insurance claims, obtaining quotes to streamlining back-office administration.

5. Property evaluations:

  • Use of computer vision and machine learning to take existing geospatial imagery to create a property information database.
  • Use of images of homes to establish the value & speeds up the insurance quote process for insurance companies & bond originators.

6. Sentiment analysis:

  • review of enormous volumes of unstructured data (videos and video transcriptions, photos, audio files, social media posts, presentations, webpages, articles, blogs) to determine positive/ negative sentiment.

7. Language Translation:

  • For forms, education, e commerce BOTs.

C) #RETAIL AI


1. Personalisation:

  • ML systems capture, analyze, and use data to personalize the shopping experience.
  • Recommend complementary products or services.

2. Clustering:

  • Algorithms are used to discover similarities/ differences in customer data (targeted marketing & segments) - present online shoppers with personalized product recommendations while adjusting pricing, vouchers, incentives in real time.

3. Predictive analytics:

  • For demand forecasting, replenishment, guidance on stock levels & new store location.

4. Sentiment analysis:

  • review of enormous volumes of unstructured data (videos and video transcriptions, photos, audio files, social media posts, presentations, webpages, articles, blogs) to determine positive/ negative sentiment.

6. Language Translation:

  • For forms, education, e commerce BOTs.

5. Object Detection

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