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Africa: The Hidden Workforce Behind AI

The machines are learning. Slowly, sure, but they are learning and we (humans) are the ones teaching them. We tell the machines how they should learn through the algorithms we write, and then feed them an enormous amount of data, so that it trains endlessly. Data labeling (the process of augmenting unlabelled data with meaningful and informative tags), is a necessary part of machine learning and sadly there’s a simple reason behind the use of a lower-wage workforce to train ML (Machine Learning) models — you only pay them half as much. The market for AI data preparation is projected to leap from $500M in 2018 to $1.2B by 2023.

Data is the only real fodder for any type of AI system. The more it trains on large amounts of ‘good data’, the faster it learns. Behind every piece of machine learning code intended to solve real issues, is a network of digital construction workers bearing the burden of building the foundation for AI — preparing data. For example, AI systems are trained to recognize objects. Data Labelers upload, categorize and cluster millions of images — just about everything from people, animals, buildings, plants, cars, signs, shapes, and things. In doing so, you now have an AI system that can begin to recognize these objects in the real world.

Again, for example, an algorithm meant to classify images of animals uses a large volume of images of different types of animals (dogs, leopards, giraffes, zebras, etc.) to train the model. These images will be labeled and classified for the model to work. A data labeler typically performs this essential function. It annotates the images with the right answers and transforms the dataset into a format suitable for machine/ deep learning.


Data Enrichment for Training ML Models

The real underlying aspect to machine intelligence is ‘the human’ in the AI loop — and it isn’t going away anytime soon either. Functions like data labeling are vital for AI quality control. Big Tech firms readily outsource these tasks to parts of the world where the minimum wage is significantly lower in order to meet extremely ambitious goals within budget. Data preparation and engineering tasks represent over 80% of the time consumed in most AI and machine learning projects. 

For instance, small data labeling companies in Kenya (and others spread across Africa) are working with large American & European firms to help them classify and organize millions of datasets. The task involves highlighting and labeling images of vehicles, traffic lights, landmarks, road signs and pedestrians captured by cameras fixed on autonomous vehicles so that these machines can become aware of the objects around them.


Bounding Boxes (tagging images for machine or deep learning models)


Image Segmentation (recognize objects of different shapes, sizes, and positions)
(source: clickworker)

Automation (the precursor to true AI) has put low-skilled jobs at supposed “extinction-level” risk for several decades now, as self-driving cars, rules-based process bots, and speech recognition will continue to exacerbate this trend. In reality, the advances of digital industrialism are not new, neither is the elimination or replacement of low-skill jobs with newer low-skill jobs. 

Sebenz.ai, a South African AI firm, is trying to create job opportunities for people throughout Africa leveraging the growing demand locally for data labelers. They have produced a Machine Learning ‘labeling game’ that allows people to earn money on their phones by labeling training data for ML models. Using this innovative approach, Sebenz is able to create labeled-data with real-time responses almost in parallel to train these models accurately.

According to the firm, it takes 10,000 hours of audio to train a speech-to-text model. With 1 data labeler, it would take 65 months, but with 10,000 people it would be ready in a few hours. In return, the data labelers are compensated around $16 per day, (minimum wage in the African continent is only a paltry $3 per day), albeit affording them the opportunity to make a better living. Most of the people drawn to data labeling jobs are often unskilled workers and live below the poverty line.

According to a 2018 KPMG research report, 5% or more of the global workforce will be replaced by automation within the next 2 years

When Silicon Valley first began importing ‘cleaned’ data in bulk at nearly a fraction of the price, then it would otherwise cost them in their own markets — it wasn’t initially received as the modest competitive advantage as it is today. However, looking ahead at the ‘future of work’ and the role of Big Tech in shaping the informal economy — the low skilled jobs fueling automation and AI will soon become automated themselves, creating newer jobs and roles for people en masse to move into, yet again.

webinar: AI for data-driven Insurers

Join our Webinar — AI for Data-driven Insurers: Challenges, Opportunities & the Way Forward hosted by our CEO, Parag Sharma as he addresses Insurance business leaders and decision-makers on April 14, 2020.

AI is shaping the future of enterprises and consumer-services in affordable and scalable ways. To learn more about how we can transform your AI journey, reach out to us at hello@mantralabsglobal.com

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Retention playbook for Insurance firms in the backdrop of financial crises

4 minutes read

Belonging to one of the oldest industries in the world, Insurance companies have weathered multiple calamities over the years and have proven themselves to be resilient entities that can truly stand the test of time. Today, however, the industry faces some of its toughest trials yet. Technology has fundamentally changed what it means to be an insurer and the cumulative effects of the pandemic coupled with a weak global economic output have impacted the industry in ways both good and bad.

Chart, line chart

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Source: Deloitte Services LP Economic Analysis

For instance, the U.S market recorded a sharp dip in GDP in the wake of the pandemic and it was expected that the economy would bounce back bringing with it a resurgent demand for all products (including insurance) across the board. It must be noted that the outlook toward insurance products changed as a result of the pandemic. Life insurance products were no longer an afterthought, although profitability in this segment declined over the years. Property-and-Casualty (P&C) insurance, especially motor insurance, continued to be a strong driver, while health insurance proved to be the fastest-growing segment with robust demand from different geographies

Simultaneously, the insurance industry finds itself on the cusp of an industry-wide shift as technology is starting to play a greater role in core operations. In particular, technologies such as AI, AR, and VR are being deployed extensively to retain customers amidst this technological and economic upheaval.

Double down on digital

For insurance firms, IT budgets were almost exclusively dedicated to maintaining legacy systems, but with the rise of InsurTech, it is imperative that firms start dedicating more of their budgets towards developing advanced capabilities such as predictive analytics, AI-driven offerings, etc. Insurance has long been an industry that makes extensive use of complex statistical and mathematical models to guide pricing and product development strategies. By incorporating the latest technological advances with the rich data they have accumulated over the years, insurance firms are poised to emerge stronger and more competitive than ever.

Using AI to curate a bespoke customer experience

Insurance has always been a low-margin affair and success in the business is primarily a function of selling the right products to the right people and reducing churn as much as possible. This is particularly important as customer retention is normally conceived as an afterthought in most industries, as evidenced in the following chart.

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        Source: econconusltancy.com

AI-powered tools (even with narrow capabilities) can do wonders for the insurance industry at large. When architected in the right manner, they can be used to automate a bulk of the standardized and automated processes that insurance companies have. AI can be used to automate and accelerate claims, assess homeowner policies via drones, and facilitate richer customer experiences through sophisticated chatbots. Such advances have a domino effect of increasing CSAT scores, boosting retention rates, reducing CACs, and ultimately improving profitability by as much as 95%.

Crafting immersive products through AR/VR

Customer retention is largely a function of how good a product is, and how effective it is in solving the customers’ pain points. In the face of increasing commodification, insurance companies that go the extra mile to make the buying process more immersive and engaging can gain a definite edge over competitors.

Globally, companies are flocking to implement AR/VR into their customer engagement strategies as it allows them to better several aspects of the customer journey in one fell swoop. Relationship building, product visualization, and highly personalized products are some of the benefits that AR/VR confers to its wielders.  

By honoring the customer sentiments of today and applying a slick AR/VR-powered veneer over its existing product layer, insurance companies can cater to a younger audience (Gen Z) by educating them about insurance products and tailoring digital delivery experiences. This could pay off in the long run by building a large customer base that could be retained and served for a much longer period.

The way forward

The Insurance industry is undergoing a shift of tectonic proportions as an older generation makes way for a new and younger one that has little to no perceptions about the industry. By investing in next-generation technologies such as AR/VR, firms can build new products to capture this new market and catapult themselves to leadership positions simply by way of keeping up with the times.

We have already seen how AR is a potential game-changer for the insurance industry. It is only a matter of time before it becomes commonplace.

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