The consistently evolving BFSI industry is data-driven, which stores huge volumes of unstructured information, most of which is not utilized properly. With the introduction of AI, machine learning, and other advanced analytics, banking institutes are now able to tap into the unstructured data that provides them with key insights about the consumers. Progress in big data and analytics has led to the emergence of new products, services, and solutions. Moreover, after the integration of machine learning with these services and solutions, the banking and financial services have become agiler and smarter. Additionally, fraud detection helps in identifying patterns in clustered data and has the ability to differentiate fraudulent activity from normal activity. Machine learning is also used to provide personalized product offering based on the patterns recognized from the user activities, which eventually leads to customer retention. The major applications of BFSI using machine learning include fraud and risk management, customer segmentation, sales and marketing campaign management, investment prediction, digital assistance, others (compliance management and credit underwriting).
Frequently Asked Questions
How does the Machine Learning Software market looks in coming years?The machine learning market expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period. Machine learning enabled solutions are being significantly adopted by organizations worldwide to enhance customer experience, ROI, and to gain a competitive edge in business operations. Moreover, in the coming years, applications of machine learning in various industry verticals is expected to rise exponentially. Technological advancement and proliferation in data generation are some of the major driving factors for the market.
Who are the target audience of the machine learning software?The key target audience includes: Machine learning/Artificial Intelligence (AI) solution and service providers, System integrators, Enterprise data center professionals, End-users/consumers/enterprise users, Telecommunication providers, Mobile network operators, Cloud service providers, Data center software vendors, IoT device/wearable device manufacturers, Cognitive and Artificial Intelligence (AI) technology experts/providers, Analytics service providers, Managed service providers, Consultants and Training and education service providers
Which are the major applications in different Industry Verticals?Applications in BFSI: Fraud and Risk Management, Investment Prediction, Sales and Marketing Campaign Management, Customer Segmentation, Digital Assistance, Others (compliance management and credit underwriting) Applications in Healthcare and Life Sciences: Disease Identification and Diagnosis, Image Analytics, Drug Discovery/Manufacturing, Personalized Treatment, Others (clinical trial research and epidemic outbreak prediction) Applications in Retail: Inventory Planning, Upsell and Cross Channel Marketing, Segmentation and Targeting, Recommendation engines, Others (customer ROI and lifetime value, and customization management) Applications in Telecommunication: Customer Analytics, Network Optimization, Network Security, Others (digital assistance/contact centers analytics and marketing campaign analytics) Applications in Government and Defense: Threat Intelligence, Autonomous Defense system, Others (sustainability and operational analytics) Applications in Manufacturing: Predictive Maintenance, Demand Forecasting, Revenue Estimation, Supply Chain Management, Others (root cause analysis and telematics) Applications in Energy and Utilities: Power/Energy Usage Analytics, Seismic Data Processing, Smart Grid Management, Carbon Emission, Others (customer specific pricing and renewable energy management)
Which is the major challenge faced by the machine learning vendors?The major issue faced by most of the organizations while incorporating machine learning in their business process is the lack of skilled employees including analytical talent, and the demand for those who can monitor analytical content is even greater.
How is the adoption trend of Machine Learning across major economies?The global machine learning market has been segmented on the basis of regions into North America, Europe, Asia Pacific (APAC), Middle East and Africa (MEA), and Latin America. North America is estimated to be the largest revenue-generating region. This is mainly because, in the developed economies of the US and Canada, there is a high focus on innovations obtained from R&D. These regions have the most competitive and rapidly changing global market in the world. The APAC region is expected to be the fastest-growing region in the market. The increased awareness for business productivity, supplemented with competently designed machine learning solutions offered by vendors present in the APAC region, has led APAC to become a highly potential market.
Which are the major factors who have boosted the growth of cloud based deployment?Flexibility, automated software updates, disaster recovery through cloud-based backup systems, increased collaboration, monitoring document version control, and data loss prevention with robust cloud storage facilities are some of the crucial benefits that have resulted in the adoption of cloud-based delivery models for machine learning software solutions and services.
1. IBM Watson Assistant ML Vs SAS Visual Data Mining and Machine LearningVS
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3. SAS Visual Data Mining and Machine Learning Vs Azure Machine Learning StudioVS
4. IBM Watson Assistant ML Vs Google Cloud DatalabVS
5. SAS Visual Data Mining and Machine Learning Vs Google Cloud DatalabVS
SAP Intelligent Robotic Process Automation controls robotic process automation, machine learning, and conversational AI in an integrated way to automate business processes with SAP Intelligent Robotic Process Automation services. The services offered help to reduce manual activities, respond to customer needs proactively, and make smarter decisions. It is capable to build intelligent bots with machine learning and conversational AI for hands-free execution and stability.
Oracle Machine Learning allows data scientists, citizen data scientists, and data analysts to work together to discover their data visually and develop analytical methodologies in the Autonomous Data Warehouse Cloud. Oracle Machine Learning consists of complementary components supporting scalable machine learning algorithms for in-database and big data environments, notebook technology, SQL and R APIs, and Hadoop/Spark environments.
Dell ML comprises an enhanced solution stack along with data science and framework optimization, enabling swift setup. The solution also leverages DataRobot - an advanced enterprise automated machine learning solution that encapsulates the knowledge, experience and best practices of the world’s leading data scientists, enabling you to quickly build accurate predictive models without previous coding and ML skills.
H2O Sparkling Water permits users to combine the quick, scalable machine learning algorithms of H2O with the capabilities of Spark. Spark is an elegant and powerful general-purpose, open-source, an in-memory platform with tremendous momentum. H2O is an in-memory platform for machine learning that is reshaping how people apply math and predictive analytics to business problems. Integrating these two open-source environments provides a seamless experience for users who want to make a query using Spark SQL, feed the results into H2O to build a model and make predictions, and then use the results again in Spark.
KNIME Analytics Platform is the open source software for creating data science. Intuitive, open, and continuously integrating new developments, KNIME makes understanding data and designing data science workflows and reusable components accessible to everyone.
The platform is designed to create potent machine learning models easy. It enables one to click through the interface for most use cases, whether one is an expert Data Scientist or a beginner. Dataiku makes it easy to leverage machine learning technologies and get instant visual and statistical feedback on model performance.
RapidMiner Auto Model provides a complete solution on a unified platform that supports the entire Machine Learning workflow from data preparation through model deployment to ongoing model management. The quick-to-learn and easy-to-use workflow designer accelerates end-to-end data science for improved productivity. With the cutting-edge tools and innovative solutions that RapidMiner provides, insights can be delivered swiftly and at scale.
Fractal Analytics enables to reveal valuable insights by accurately recognizing objects in images and videos. From surveilling people in real-time at events to detecting if products are in the right place in shopping aisles, AI can drive value in many ways. This helps in creating in-depth analyses by placing image objects into relevant segments. Fractal Analytics AI-based algorithms help insurers analyze home and auto damage to create more accurate claims for customers.
TIBCO Software is AI-powered, search-driven experience with built-in data wrangling and advanced analytics. It connects the creativity of the entire team, citizens to experts. It is capable to combine AutoML, intuitive drag-and-drop workflows, and embedded Jupyter Notebooks that make creating and sharing reusable modules easy.
Domino is a data science platform that allows data science teams to quickly develop and deploy models that drive ground-breaking innovation and competitive advantage. The platform automates DevOps for data science so that one can spend more time doing research and test more ideas faster. Enables automatic tracking of work for easy reproducibility, reusability, and collaboration.
The analytics platform that boasts a built-in machine learning engine provides a wide variety of descriptive, predictive and prescriptive analytics; autonomous decision making and visualization tools. The platform is compatible with SQL, R, and Python, and can interface with visualization and BI tools like RStudio, SAS and Jupyter.
Luminoso Score Drivers, a machine learning-powered solution helps companies intelligently automate the process of finding drivers in qualitative and quantitative feedback from their customers and employees. Score Drivers analyzes unstructured reviews and survey feedback and reveals how this unstructured data correlates with quantitative ratings.