Grand Opportunity

Artificial Intelligence

Navigating through the technology hype

What is the grand opportunity?

The emergence of cheap and powerful cloud computing and data storage platforms, and the availability of sophisticated open-source AI algorithms, are fuelling dramatic growth in the development of commercial and industrial applications using AI technologies. If you want to capitalise on these game-changing AI technologies, CamIn will guide you to make the right strategic decisions for your digital transformation initiatives.
490

Billion dollar cost savings in front office banking applications provided by the adoption of artificial intelligence technologies by 2030

90

Percent cost reductions delivered by using natural language processing to automate formerly manual document review processes.

Which Industries Will Benefit The Most From Artificial Intelligence?

Download our latest insight identifying which industries will benefit the most from the artificial intelligence technologies over the next two years.
Which Industries Will Benefit The Most From Artificial Intelligence?

Deep Learning Neural Networks

Voice-based Transactional Virtual Assistants

Voice-based transactional virtual assistant technology can replace human assistants in roles that involve providing information or performing automated tasks more accurately and efficiently. For example, applications of this technology in the banking industry could simplify tedious tasks, such as looking up a specific account transaction, by requesting the transaction’s details conversationally, rather than navigating a website manually to find the specific transaction and its details. The applicability of this technology is broad, and applications relying on it are expected to spread to other industries that involve frequent customer engagement such as the healthcare, retail, professional services, and travel industries.

Computer Vision

Computer vision algorithms powered by deep learning enable automated and accurate inventory counting and the detection of subtle flaws in manufactured parts. In the agriculture industry, farmers can already diagnose crop diseases from the analysis of satellite imagery, together with cure recommendations. The benefits to both farmers and the agrochemical industry from this application include more rapid and less expensive diagnostic processes than the traditional procedures which require on-site inspections of the affected crops by trained agronomists. Uses of this technology are also spreading to the mobility, retail, and logistics industries.

Machine Learning Classification Algorithms

Random Forest

Random forest algorithms can be trained to identify errors and predict problems in complex systems. For example, problematic conditions on oil and gas pipelines, including leaks, can now be detected automatically by these systems through the analysis of real-time pressure and flow data. This technology-based solution obviates the old, manual, expensive, and inaccurate methods for detecting pipeline leaks, which required visual inspection of miles of pipeline by pilots in light aircraft. These algorithms are also being employed to automatically identify anomalous conditions with applications as varied as medicine, agriculture, and cybersecurity.

k-Nearest Neighbour

k-Nearest Neighbour algorithms can be used to find clusters of stocks with similar trading patterns. Quantitative traders and investors can use these algorithms to automate investment decisions in correlated instruments, which were previously calculated using manually-maintained spreadsheets.  Benefits of these algorithms include rapid, accurate responses to changing market conditions, such as those achieved by high-frequency trading systems—which are consistently more profitable than human-operated trading strategies. These algorithms are also being used in retail contexts to understand customer behaviour and in medical contexts to study diseases.

Digital Twins

Operational Simulation

An operations simulation is a unique digital replication of a physical system. Oil and gas production companies are instrumenting their drilling equipment with IoT sensors to collect data for digital twins, which can then predict maintenance needs using AI and machine learning. This digital approach can provide a large cost savings over the old methods involving manually inspecting and testing equipment to assess its condition. Operational digital twins are being adopted in a diverse range of industries, including the aerospace, manufacturing, and infrastructure industries.

Design Simulations

Digital twin design simulations use simulated data from real sensors placed across “smart cities” to evaluate an existing infrastructure’s ability to perform with additional features or in future scenarios. City planners can test and optimise designs in the simulation, replacing their previous processes relying on theory or statistical simulations, and gain the benefits of realistic testing of city designs and/or the introduction of new infrastructure. Other industries benefiting from design simulation include the aerospace and logistics industries.

Big Data Analytics

Market Basket Analysis

Market basket analysis (MBA) identifies correlated buying patterns of different products, and is unique in power to scour the history of all shoppers for these correlations. Online retailers use MBA as the basis for recommender systems suggesting an additional purchase. Formerly, it was entirely up to the buyer or salesperson to search the catalogue of products to identify potential products that could be combined as additional purchases, so the new technology enables faster and more accurate decisions. Other industries are expected to begin using this class of algorithms, including the food, pharmaceuticals, and travel industries.

Support Vector Machine Algorithms

Support Vector Machine Algorithms can be used to predict specific customer behaviours based on historical patterns of all customer behaviour and characteristics. Banks and insurance companies have begun using this technology to identify customers at risk of closing their accounts, and then proactively offering these customers incentives to maintain their accounts. The benefits achieved include improved customer retention than with traditional manual methods, which relied on a fragmented array of evidence and customer reactions. Other industries that will find valuable applications for this technology include the healthcare, professional services, and transportation industries.

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