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.

Machine Learning Software in BFSI

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IBM Watson Assistant ML
IBM Watson machine learning software helps enterprises to use their data to create, train, and deploy self-learning models. It also helps users in building analytical models and neural networks. IBM data science experience is a cloud-based, social workspace that helps data professionals to consolidate, create, and collaborate across multiple open sources tools, such as R and Python.
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SAS Visual Data Mining and Machine Learning
SAS best machine learning software offers major features like automated model tuning, powerful data manipulation and management, flexible web-based programming environment, integrated text analytics, model assessment and scoring, and modern statistical, data mining, and machine-learning techniques. It facilitates the end-to-end data mining and machine learning process with a visual and programming interface. It also boosts analytics teams of all skill levels with a simple yet powerful and automated way to tackle all tasks in the analytics life cycle.
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Azure Machine Learning Studio
Microsoft Azure Machine Learning is a fully managed machine learning software service for advanced analytics in the cloud. It enables enterprises to build advanced analytic web services quickly and eradicate much of the heavy lifting associated with deploying machine learning in modern data-driven applications.
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Google Cloud Datalab
Google Cloud AutoML Machine Learning Software includes portable models, notebook developer experience, scalable service, managed service, HyperTune, and the ability to discover and share samples. The company's Advanced Solutions Lab (ASL) supports businesses to partner with Google Cloud and apply machine learning software to tackle high-impact business challenges. The solution offers a unique opportunity for technical teams to understand from Google’s machine learning software experts.
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SAP Leonardo Machine Learning Foundation

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.

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Amazon SageMaker
Amazon Machine Learning Software Amazon Forecast eases the job of obtaining predictions for user’s applications using simple APIs without the need to implement custom prediction generation code or handle any infrastructure. It is considered to be highly scalable, and has the ability to generate billions of predictions on a daily basis and can serve those predictions in real-time and at a high throughput as well.
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Baidu
Baidu’s open-source deep learning platform, PaddlePaddle (Parallel Distributed Deep Learning) supports neural network architectures, including convolutional neural networks and recurrent neural networks. The platform is fully scalable and is designed to enhance mathematical operations using BLAS libraries, including Intel MKL, ATLAS, OpenBLAS and cuBLAS. The company has made most of its software and systems open source and provided access to it on an “as-a-service” basis. 
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FAIR ISAAC CORPORATION (FICO)
FICO has introduced new explainable artificial intelligence toolkit (xAI Toolkit) which meets customers’ increasing demand for industry-leading artificial intelligence. This solution enhances decision performance by integrating predictive and prescriptive models directly into real-time business operations to create faster, more impactful business outcomes. It also offers intuitive end-user experience and delivers direct and immediate access to data and insights dynamically.
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Oracle Machine Learning

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.

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EMC NetWorker

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.

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H2OAI

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.

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KNIME Analytics Platform

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.

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Dataiku DSS

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.

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RapidMiner

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.

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ALPINE DATA LABS
With organizations understanding the importance of Machine Learning technologies, it is moving out of the realm of untouchable subjects. Alpine Data Labs provides the data scientists with big data machine learning capabilities along with a high level of governance required by each and every organization today. The company is based on the philosophy of tracking movement or lack of movement of data. Alpine is known for its ability to access data sources directly.
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fractal

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.

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TIBCO Data Science

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.

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Domino

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.

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Teradata Vantage

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.

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Luminoso Analytics

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.

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