Artificial intelligence (AI) has grown exponentially in recent years. It’s able to match and, in some cases, exceed human accuracy at such tasks as reading comprehension, image recognition, and text translation. However, an area that is seeing massive opportunities that weren’t possible previously is GIS (geographic information systems). GIS is a computer system that displays and analyzes geographically referenced data.

In broad terms, AI is the capacity for computers to perform tasks that usually require some degree of human intelligence. Machine learning is an approach that can perform this method. It utilizes algorithms to acquire information from the data to provide the necessary answers. For instance, machine learning can help with automated territory map generation.

Although machine learning has been a crucial part of GIS software in Clustering, Classification, and Geographically Weighted Regression, spatial analysis can now go further by using deep learning tools. Let’s look at some cases of deep learning’s application in geographically referenced information.

Deep Learning’s Application in Geographically Referenced Information

• Image Classification

Deep Learning can be used to determine whether a photo is type A or B so as to categorize geotagged photos.

• Instance Segmentation

Instance segmentation is a more exact Object Detection method from which the precise shape of an object in an image can be derived. Using this method, GIS can be combined with LiDAR data to recreate buildings in 3D.

• Semantic Segmentation

This process classifies each image pixel, so it belongs to a specific class. In GIS, this approach can be used for Land Cover Classification.

• Object Detection

Object detection is a computational approach that finds objects within an image by coding and locating them. In GIS, combining this process with aerial photography, satellite imaging, or drone photography makes it possible to map objects of interest.

Machine Learning and Location Intelligence for Real-World Applications of GIS 

Using location intelligence, GIS technology, and Machine Learning automation, industries are becoming more innovative and gaining real-time insight. By combining these methods, businesses are gaining the ability to map, analyze, and share data in the context of location. For instance, they can spot trends and make predictions to support market assessments, site selection, asset tracking, risk management, and various other central business needs. Simply put, machine learning manages complex data, and location intelligence provides the data with crucial location context. 

Let’s look at some real-world examples of how these tools are being applied in various industries.

• Retail Industry

In the retail industry, machine learning and location intelligence have many applications. Retailers can use these tools for site selection, optimizing their supply chain, and location-based advertising. These tools can also help with customer support, providing personalized customer experiences, and setting prices.

• Government Agencies

Government agencies apply machine learning algorithms on georeferenced satellite and drone imagery. This allows them to automate model growth scenarios and fieldwork, predict crop yields and assess the health of crops in real-time.

• Logistics

Drivers, route planners, and operations managers can use AI to track assets in real-time, anticipate future supply needs, accurately predict arrival times, and fill in the gaps in road network databases.

• Finance

Machine learning helps banks and financial analysts detect fraud, perform predictive risk assessments, and plan either one branch location or a network of multiple locations.about:blank

• Manufacturing

When it comes to the manufacturing industry, manufacturers can use AI systems to automate inspections and quality control, optimize supply chain logistics, plan predictive maintenance, and flag any unusual activities that can slow production.

Conclusion

Artificial Intelligence is changing the GIS landscape by using deep machine learning to improve the analysis of geographically referenced information. AI, specifically machine learning and location intelligence, is also being used to help various industries analyze and improve their processes.

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The availability of massive data is driving the FinTech industry to harness the power of the hidden gems that only data analytics can deliver.

The FinTech industry has witnessed a massive shift owing to digital transformation. From banks to e-commerce platforms, astronomical amounts of data are being generated in the form of transactional and non-transactional data.

Ruled by the power of algorithms and data science, it is enabling businesses to spot consumer trends, and empowering them to create real-time growth opportunities. In a fiercely competitive environment like the payments industry, data science approaches have already matured.

Despite the industry being highly regulated, businesses can attain an edge over their competition by leveraging powerful insights unearthed through data science. The availability of massive data is driving the FinTech industry to harness the power of the hidden gems that only data analytics can deliver.

Here are the top three ways in which data science is being leveraged by the FinTech industry:

1. Fraud detection and prevention– The number of frauds, as well as their new mechanisms, make it difficult for traditional rule-based approaches to detect them. A scalable way to keep track of fraud is to use data science. Data science techniques are widely utilized to identify and predict fraudulent financial transactions. Gradient boosting models are a popular choice. If interpretability is an important factor, more simple models like logistic regression could be used, or advanced techniques like Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanation (SHAP) could be tapped to explain more complex models. Owing to an exponential rise in the number of daily online transactions, there is a need for FinTech players to place fraud prevention on the top of their agenda. Employing the right mix of predictive analysis, behavioral profiling, and real-time detection, data science can enable financial organizations to keep abreast of new ways of committing fraud with low to no manual intervention in an automated fashion using algorithmic approaches. While fraud detection and prevention are critical aspects that data science can aid, their true potential and capability extend far beyond these functions.
2. Credit scoring models- Assigning a credit score to people who quantify the likelihood of default is an extremely important part of FinTech companies dealing with providing loans. In some emerging economies, people prefer not to have bank accounts, leading to discrepancies in accounting transactional details holistically. This has posed a significant challenge to the FinTech industry to assign them a credit score. Businesses are harnessing the power of data science techniques like profiling based on psychographic surveys to go beyond the traditional credit scoring methods which require a banking history. From geocoding, analyzing SMS messages to psychographic surveys, these data points could serve as a substitute for traditional banking history and might predict likely defaulters. Technologies like machine learning are playing a key role in providing loans to people who are not yet in the formal banking sector.
3. Customer lifetime value models- To grow more, businesses need to sell more, which can be achieved by acquiring new customers. A recent Gartner survey revealed that 44% of CMOs expect marketing budgets to decrease because of COVID-19[i]. This will mean an increased focus on ensuring that customer acquisition costs (CAC) are reduced. With the dynamics of business changing rapidly and revolving around its customers, it is very critical to get to know a customer’s lifetime value (CLV). CLV enables businesses to concentrate their efforts on their best clients. Better their understanding of CLV, the better they can employ their strategies to retain their most profitable customers. Another efficient way to apply this would be to use machine learning models to calculate customer lifetime value (CLTV models). The CLTV can ensure that customers identical to existing customers with a higher CAC than their CLTV are not acquired again.

Today consumers have numerous payment methods at their disposal, there is not a single value-based ecosystem that effectively connects cash, digital, and loyalty rewards today. The FinTech Industry is enormous in its own right, and by employing the advanced methods offered by data science it can scale hitherto unknown heights of growth and profit.

Herein lies a crucial opportunity for businesses to drive engagement, higher customer satisfaction, and elevated experiences.

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*The retail industry has been one of worst-hit industries by the global pandemic.

*India is going to take a leadership role in AI for the world.

*There has never been a better time for AI ecosystem players and especially startups offering AI solutions.

The McKinsey Global Institute has recently studied economic statistics from the United Nations, the World Bank, and the World Economic Forum and estimated that by 2030, AI can add 16 percent—or around $13 trillion—to the global economy and AI will boost global GDP by $15.7 billion. Since 2000, AI venture investments have risen six times and McKinsey also estimates that by 2030, at least 70 percent of businesses are likely to have implemented at least one type of AI technology.

In India comparatively, Artificial Intelligence is a growing industry sector, as a report by Computer Vision Market reveals and stands at $6.4 billion as of 2030.  36.2 percent of this demand is coming from MNCs, GICs, and Captive firms with a market valuation of $2.3 billion. The future will see a vast number of other industries such as healthcare, high-tech manufacturing, and semiconductor companies adopting AI making it one of the most attractive and fastest-growing technology sectors over the next decade.

The retail industry has been one of worst-hit industries by the global pandemic and after an initial knee-jerk pause during the lockdowns, we see a sudden spike in retail brands (across categories of products) globally wanting to adopt and embrace digital transformation initiatives. The age-old model of customer service via call-centre’s has been disrupted permanently by Covid-19 and moving to AI solutions that can help retail businesses manage both exponential rise in call volumes while maintaining very high service level quality is the new emerging opportunity. It is, therefore, not a surprise that the conversational AI market alone is expected to grow at 32 percent CAGR to $9.4 billion by 2024 according to market research company markets & markets.

The Indian Government is playing a significant role as well and recently joined a Multinational Alliance on Artificial Intelligence that includes big nations such as the United States, the UK and Australia. Also, India has launched a Nationwide Artificial Intelligence Plan to bring the best of ideas and expertise from all fields and industries together making it one of the most happening and promising technology ecosystems to watch out for.

The National policymakers substantially expanded public AI funding through promises, such as increased R&D spending, the creation of industrial and construction funds by startups, network and technology investments, and public procurement relevant to AI. The government is also creating and fostering AI through numerous variations of public-private academies; the creation of technology parks and the connection between big companies and startups.

NASSCOM estimates that by 2022, 46% of the Indian workforce will be employed in completely new industries, which do not exist, or jobs that have drastically changed skill sets. NITI Ayog reports estimate that in India demand for AI and computer learners increased 60 percent in 2018. An Independent study has also stated that in 2020 India faced a demand-supply gap of 2,00,000d data analytics experts.

While global businesses have been at the forefront of adopting emerging technologies such as AI, blockchain, and IoT, Indian businesses have been traditionally late technology adopters. Owing to an in general risk-averse mindset culturally, only once a technology has been proven and tested at scale, Indian businesses are willing to adopt it and we see a similar trend even in AI and AI-based solutions.

However, the COVID-19 pandemic and subsequent lockdowns have reversed this trend completely and we see Indian businesses realize that adopting emerging technologies in today’s world is no longer an option that can be delayed any further. In many cases it can be the deciding factor between thriving, surviving, or perishing for the business. For example, at AskSid, where we offer a digital shopping assistant that help retail brands sell more, we have seen a drastic increase in demand in the last 6 months leading to a 3X-5X jump in our opportunity pipeline.

This increased adoption within businesses is being driven by the rapid usage and adoption of AI by consumers in their daily life. Alexa, Siri, Google as voice assistants are house-hold names today both in India and outside and the expectations to get served instantly in a personalized manner is considered totally normal by today’s consumers. Any business which fails to deliver on this high standard of customer experience which consumers demand carries the serious threat of becoming redundant very fast and therefore embracing digital transformation using emerging technologies such as AI is no longer an option for Indian businesses.To conclude the overall scenario, one thing that is evident in the global vs. Indian AI debate is that India is now no longer a laggard,

• India is going to take a leadership role in AI for the world. Indeed, through zealous technological advances and consistent R&D India can be the next artificial intelligence superpower. Indian firms and AI entrepreneurs must begin to invest more in research and to grow AI products and solutions from India.
• There has never been a better time for AI ecosystem players and especially startups offering AI solutions. There lie a huge market and humongous opportunity for budding entrepreneurs to tap into.

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