It’s fascinating to think about the power we have in our hand. Today, the tiny transistors in the smartwatch, smartphones are capable of more advanced computing than scientists of the 1950s could’ve ever dreamed. However, even with the phenomenal advancements we made in technology, there remain some problems that classical computers just can’t solve. And many believe quantum computing/computers are the answer.

QC (Quantum Computing) – Why & How?

A normal computer chip uses bits. These are like tiny switches, that can either be zero (off position) or in one (on position). For most of the operations this works good enough, but it doesn’t reflect the way the universe actually works. In real world, things aren’t just on or off instead they’re in flux and in fields. And even our best supercomputers aren’t very good at dealing with uncertainty.

However, QC (Quantum Computers) can leverage the quantum mechanical phenomena to manipulate information. Instead of bits, quantum computers use qubits. Rather than just being 0 or 1, qubits can also be in what’s called ‘superposition’ – where they’re both 0 and 1 at the same time, or anywhere on a spectrum in-between. This means that a computer using qubits can store an enormous amount of information and uses less energy. For example, if we ask a normal computer to figure its way out of a maze, it will try every single path in turn, ruling them all out individually until it finds the right one. A quantum computer can go down every path of the maze at once. It can hold uncertainty in its head, figuratively speaking!

By entering into this quantum area of computing where the classical laws of physics no longer apply, we are able to create processors that are significantly faster (a million times more) than the ones we use today. Sounds fantastic, but the challenge is that quantum computing is also incredibly complex.

How QC will change our world:

With QC , we are venturing into an entirely new realm of physics and there will be solutions and uses for many things which we have not even conceptualized yet. But when you consider how much classical computers revolutionized our world with a relatively simple use of bits and two options of 0 or 1, one can imagine the extraordinary possibilities when the processing power of qubits that can perform millions of calculations at the same moment.

However, what we do know is that it will be game-changing for every industry and will have a huge impact in the way we do business, design and develop new medicine, space exploration, logistics optimization, financial modeling, safeguard our data, predict weather events and climate changes, etc.

It’s no coincidence that some of the world’s most influential companies like IBM, Google, etc and the world’s governments are paying attention/investing heavily in QC technologies. So are the startups in Silicon Valley working on QC algorithms. They are expecting QC to change our world because it will allow us to solve problems and experience efficiencies that aren’t possible today.

In my upcoming post, I will dig deeper into how QC will potentially reshape our future…

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It is apparent that AI is one technology that will help businesses to be innovate, agile, innovate hence scale. The companies that become “AI-inside” will have the ability to synthesize information, the capacity to learn, and the capability to deliver insights at scale. According to new data from IDC Worldwide spending on artificial intelligence (AI) is forecasted to double over the coming for years to hit $110 billion by 2024!

As adopting AI is becomes a ‘must’, companies are quickly learning that AI doesn’t just scale solutions, but also scales risk dimension. Companies need a clear plan to deal with the new facet where data and AI ethics can pose problems if not addressed closely.

It’s interesting to note that in 2020, 50% of customers say that they benefited from personalized recommendations and suggestions, up from 26% in 2019.The share of customers trusting AI interactions is on the rise and will continue to do so, though it is still less than half Percentage of customers who trust AI engagements.

Enterprise adoption of Ethical AI:

Google was one of the first companies to pledge that its AI will only ever be used ethically – ie it will never be engineered to become a weapon. Google has published its own ethical code of practice in 2018 in response to widespread criticism over its relationship with the US government’s weapon program, which later was confirmed by its CEO in a public statement.

Google, Facebook, Amazon, Microsoft and IBM have come together to develop best practice for AI, with a big part of that examining how AI should be – and can be – used ethically as well as sharing ideas on educating the public about the uses of AI and other issues surrounding the technology.

The consortium explained further: “This partnership on AI will conduct research, organize discussions, provide thought leadership, consult with relevant third parties, respond to questions from the public and media, and create educational material that advance the understanding of AI technologies including machine perception, learning, and automated reasoning.“

Microsoft is also working closely with the European Union on the development of an AI regulatory framework, considered to be the first of its kind. The hope is to create something that offers the same sort of guardrails and principles that GDPR establishes around the use of data.

Tools that promote Neutrality ( sensitize Bias)

There are several tools out there to help developers and software designers in identify biases in their AI-based applications like:

• AI Fairness 360 – An extensible open source toolkit that can help you examine, report, and mitigate discrimination and bias in machine learning models throughout the AI application lifecycle.
• Adversarial Robustness Toolbox (ART) – A Python library for Machine Learning Security. ART provides tools that enable developers and researchers to evaluate, defend, certify and verify machine learning models and applications against the adversarial threats of Evasion, Poisoning, Extraction, and Inference.
• Sogeti’s Artificial Data Amplifier (ADA) – An AI-driven synthetic data generating solution, aids in combating and mitigating bias by generating synthetic data that can rebalance the original dataset, providing a more fair representation of minority groups in the data.
• Glassbox – Leverages open source libraries to check data sufficiency, quality, validates AI models against preset industry benchmarks, and measures a model’s resilience towards perturbations in inputs. Reports generated by the solution can help developers and users of AI fine-tune their models for accuracy, recalibrate them for new environments, and retrain them for new features and algorithms, thereby improving their resiliency.
• AI-RFX Procurement Framework – Created by Institute for Ethical AI & Machine Learning, is a set of templates to empower industry practitioners to raise the bar for AI safety, quality and performance.
• Not Equal Network – Aims to foster new collaborations in order to create the conditions for digital technology to support social justice, and develop a path for inclusive digital innovation and a fairer future for all in and through the digital economy.

Conclusion:

Creating “Accountable” AI is key for all the stakeholders in the business landscape. Bias is one of the major issue with AI, as its developed based on the choices of the researchers involved. This effectively makes it impossible to create a system that’s entirely “neutral” . Creating the need for ethical AI framework with built in accountability is key – not just for improving customer engagement and mitigating risks of unethical interactions, but also for actively using data and AI for the good purpose.

#AI #ML #Ethical #Bias #Neutral #DeepLearning #UserExperience #DigitalDisruption #FraudDetection #ConversationalAI #CognitiveIntelligence #NLP #RPA #ProductInnovation

#Strategy #Management #Consulting #Transformation #Technology #Outsourcing #CreativeDisruptions #EternalQuest #FindingTruth

In simple terms, Artificial Intelligence (AI) is a set of computerized tools which have been designed to achieve business objectives. AI can help automate labor intensive processes, leading to increased efficiency, lower costs. Companies are using AI to analyze the data they have on customers to predict their behavior, understand preferences and optimize price and product offerings. 

AI has helped in transforming large companies and industries by identifying new avenues of growth and profitability. Being an old and highly regulated industry that insurance is, companies are moving away from the manual, paper based processes to adopting modern technologies. AI technology have made great inroads in different sectors of the insurance industry and are lowering costs while increasing efficiency and improving the customer experience. Slowly but surely, the industry is transforming.  

One of the key reason why AI will be crucial is the ever increasing “datafication” of business interactions, private life and public life. AI can be used to effectively understand drivers and trends in the insurance industry. According to Deloitte, AI is helping insurers by predicting risk with greater accuracy, customize products and use enhanced foresight to rapidly deploy new products thus reducing time to market to toe along with market/business agility.  

The European insurance sector is the second in the world, accounting for 31.6% of global insurance premiums, behind Asia-Pacific (32.4%) and ahead of North America (30.8%). France, Germany and the United Kingdom are the largest players on the European market. Consulting firm McKinsey estimates a potential annual revenue of $1.1 trillion, if artificial intelligence is fully applied to the insurance industry. 

According to a recent report by Bain & Company, the EU region is experiencing an insurance boom, as well as significant digital disruptions. Due to the stiff competition from emerging new players, insurance companies are implementing artificial intelligence (AI) powered tools to stay differentiated. Insurers are using advanced analytics, machine learning and other AI-driven tools to elevate the customer experience and also to compete with agile digital newbies. According to a study by PwC, more than 80 percent of insurance CEOs said AI was already a part of their business model or would be within the next three years. 

How are Insurers Adopting AI? 

Here are four major areas where insurers are implement AI to improve customer engagement, combat fraud and streamline business processes. 

• Fraud Detection: 

According to the Association of British Insurers, more than half a million insurance frauds, totaling £1.3 billion, were detected in the UK during 2017, while in the US FBI estimates insurance fraud robs the US insurance industry of a whopping $40 billion a year. Insurers are turning to AI driven predictive analysis and machine-learning tools to identify and flag unusual claims, anomalies, patterns thus potentially helping them to reduce the fraudulent claims to protect the bottomline.

• Customer Profiling & Segmentation: 

By automating and applying cognitive intelligence to their processes, insurance companies are leveraging the benefits of profiling. Insurers can unify and derive insights from their internal and external customer data, and develop a comprehensive picture of their customers, such as their insurance needs, interests and life stages, for more effective targeting. Insurers can segment their audience based on these attributes, and use deep learning to predict the conversion rates. With deeper insight, insurers can recommend the relevant products for each customer segment.    

• Product & Policy Design 

One of the key area insurance companies are using AI is improving their product and policy design. Personalization and providing flexibility in choosing these products is very key where AI powered chat-bots and social media listening play a crucial role in this. By analyzing the massive data collected from different sources, insurers can use machine learning to discover customer trends and interests in real time. These insights are then being used to develop and improve product and policy design. 

Additional innovations with AI/ML under the hood has helped Insurance companies to offer Usage-based insurance (UBI) model which gives customers significant cost benefits. To know more about UBI check out my earlier post – UBI, On-demand insurance is gaining momentum!

• Underwriting & Claims Assessment 

The process of underwriting is often considered as an art based on personal judgment, but AI technologies have entered into this area of insurance, making it a scientific process. Insurers are now using advanced analytics and machine learning, as well as additional sources such as satellites and the IoT devices, to get a more holistic view. Telematic devices, a wearable technology for auto insurance brings significant benefits by reducing the premium and risk exposure thus being a win-win formula for both Insured and Insurer.      

Insurance firms using image processing AI to analyze the extent of the damaged vehicle, which helps them to generate a list of repairable and replaceable parts that were damaged in the accident. Within a few minutes, the assessment and the cost are given to the customer, which they can either accept or reject. If the assessment gets accepted, the payment is made within couple of hours directly to the customer’s bank account. Which also means happy customers thus leading to stickiness which is essential in an industry where customer attrition is significant!

Conclusion: 

From fraud detection to underwriting, AI technologies are re-imagining every aspect of insurance industry. By reducing the risks and streamlining processes, it’s helping companies increase efficiency and deliver more personalized products and services. While some insurance companies have started implementing AI, there still remains many companies which are yet to find the right use cases for them. To leap from AI mystery to mastery, practitioners need to bridge the gap in understanding the technology’s inner workings and its business value proposition. 

#AI #ML #Insurance #Insurtech #DigitalDisruption #ImageProcessing #DeepLearning #UserExperience #FraudDetection #ConversationalAI #CustomerProfiling # #CognitiveIntelligence #NLP #RPA #ProductInnovation

#Strategy #Management #Consulting #Transformation #Technology #Outsourcing #CreativeDisruptions #EternalQuest #FindingTruth