With the advent of AI & Analytics, every industry is reinventing itself, and banking and financial institutions are no exception. It is now a need of the hour to derive insights from the heaps of data that exists in the databases to redefine customer experience and drive business use cases to mitigate financial risk and maximize return on investment.
Leverage machine learning and deep learning to build credit scoring models that will analyze past historical data to predict the probability of customer default, revamp credit approval and optimize portfolio management. Minimize fraud by serving customers who rightfully deserve credit and save time and resources that would otherwise be spent on detecting and handling fraudulent cases.
Train and deploy models that can quickly and easily detect suspicious and fraudulent activities in real time to combat money laundering. There are increased regulations from governments and financial institutions worldwide to prevent security breaches. Generate insights from data residing in data lakes and data pipelines to learn about fraudulent and malicious behavior and prevent customers’ financial data.
Analyze customer journeys to learn about customers’ tastes, preferences, and buying patterns to deliver personalized services. Take personalization to a whole new level by using sophisticated Natural language Processing (NLP) techniques such as sentiment analysis and information extraction to understand human emotions and form long-lasting customer relationships through continuous innovation.
View more use casesIf the Covid-19 pandemic has taught us one thing, it’s that connectivity matters the most. With the roll-out of 5G technology, IoT, and widespread adoption of cloud computing, telcos lie at the forefront of digital disruption. The industry as a whole has to pivot to leverage AI & Machine Learning to adapt to the fast-changing business landscape to drive revenue and growth.
Telcos have to handle a large volume of trouble tickets each day that can negatively impact customer experience if not resolved quickly. Classification algorithms such as decision trees or random forests can help quickly establish the root cause. This will empower the NOC team to optimize resource allocation and take measures in advance to mitigate risks.
Customers put their trust by sharing their personal and financial data and it is incumbent on telcos to reciprocate that trust by deploying effective models that can detect malicious behavior. Illicit activities, such as identity theft, fake authorization, and account cloning are on the rise. Unsupervised machine learning techniques could be leveraged to detect such anomalies in real-time so action could be taken to prevent such fraudulent activities.
Telcos have a global presence with operations in different markets. Hence, it is extremely important to be able to uniquely identify the customers’ needs, preferences, and behavior in each market. By delivering personalized products and targeted campaigns, data science analytics can help telcos form deeper, meaning and long-lasting customer relationships that will enhance brand image and reduce churn.
View more use casesUnderstanding consumer behavior has become quite complex with the advent of big data. Only those businesses will prosper who can make use of the enormous amounts of customer data that they have and convert it into actionable insights to engage with potential and existing customers on a more personalized level. Consequently, marketers need to up the ante to stay relevant and impactful.
It has become extremely important to understand why your customers are behaving the way they are. Gone are the days when you could just build buyer personas based on descriptive statistics. Explainable AI helps marketers understand the predictions generated by machine learning models so they could deliver a 360-degree customer experience to boost revenue.
This is the age of social media. Customers are sharing their feelings and emotions towards your product on social media channels. No, it is up to you to passively listen to those comments or actively mine them using sentiment analysis techniques to realign your goals and strategies that drive maximum impact and lets you stay on top of mind of your customer base.
Acquiring new customers is costlier than retaining existing ones. Hence, it makes sense to identify opportunities to cross-sell and up-sell to your existing customer base as it will keep costs down and profits up. Clustering and association rules which are unsupervised machine learning techniques can map customer journeys to identify the relationship between items bought by a customer so you can promote and cross-promote products to similar buyer personas.
View more use casesThe manufacturing industry has undergone a dramatic transformation over the decades due to increased robotization and automation. Now, Data Science presents itself as the next step in evolution as companies face increased competition from global supply chains and look for innovation to cut costs, boost revenue and maximize productivity by harnessing the power of AI & Analytics.
With devices connected to the internet and providing a continuous stream of information, businesses can mine useful insights about product usage and be more proactive in developing features that would cater to the needs of the customers. Machine Learning models can ingest the sensor data and make predictions that can help companies continuously improve their products.
Equipment failure or breakdown can lead to delays or even complete shutdowns, causing businesses to incur heavy losses. Build and optimize classification models that can analyze time-series data to predict equipment failure before they happen so as a company, you can plan in advance for any unforeseen delays in production to meet customer demand.
Leverage big data to train and deploy models to optimize the production processes and accelerate product delivery to global markets. Manufacturing firms can incur huge financial losses due to delays and hence there is a need to prepare contingency plans for such delays. Techniques such as survival analysis and regression models could empower companies to predict lags and accurate delivery times.
View more use casesPeople are increasingly looking towards their governments and state institutions to provide them with resources and equal opportunities to improve their standard of living. AI is at the heart of decision-making when it comes to allocation and efficient utilization of resources to transform societies in all spheres of life through technology and innovation.
Governments can build models based on data collected from the healthcare institutions to help them project the number of hospitals, clinics, rehabilitation centers, and other facilities needed to adequately address the needs of the people. Geographical data could tell which areas need special attention. With the threats posed by pandemics like Covid-19, it has become increasingly important to be able to predict the outbreak of such diseases well in advance using AI.
Law enforcement agencies can benefit hugely by analyzing crime statistics and finding correlations and hidden patterns. AI & Analytics could be leveraged to deploy models that can continuously learn from data and update themselves. The security and law enforcement agencies can turn these insights into actionable intelligence to be better able to respond to violent incidents, cybersecurity threats, and criminal activity.
With trillions of dollars being spent on defense by governments worldwide, it has become more important than ever to make the defense institutions more efficient. AI-powered solutions can help governments monitor and track tier borders to detect infiltrations, safeguard their air and naval bases and upgrade equipment and logistics to significantly improve response time in case of a possible threat.
View more use casesBusinesses across all industry verticals be it retail, manufacturing, distribution, or logistics are interested in optimizing their supply chains and making them less vulnerable to the ever-changing market dynamics due to global disruption, lack of transparency, and uncertainty. Data Science holds the key to a whole new world of endless possibilities to make supply chains more resilient and efficient.
Build data lakes and data pipelines, whichever best suits your business needs to accurately predict the amount of stock you should have in your inventory to balance demand and supply. Leverage machine learning and deep learning models to analyze past historical trends and build reports to uncover hidden trends and patterns to prevent over or under-stocking and always stay cash-flow positive.
Gather and analyze heaps of data from multiple sources to build statistical models with the help of machine learning to calculate the optimal and most cost-effective route of delivery for shipments. Moreover, track and monitor goods and services through their complete logistics journey in real-time and keep yourself updated about any damages that might occur due to bad weather or poor road conditions.
Having equipment up and running is key to ensuring a smooth supply chain. With the help of AI and advanced analytics that take advantage of distributed systems to process huge volumes of data in parallel, maintenance costs can be significantly reduced by detecting in advance faults in the production lines and taking the necessary measures to rectify the equipment before it breaks down.
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