Name: Financial Fraud Mitigation with Data Analytics
Start: 2021-05-25T05:53:00Z
End: 2021-05-25T05:53:58.000Z
Location: BrightTALK
Rating: 0

Altair is a global technology company that provides software and cloud solutions in the areas of data analytics, simulation, and high-performance computing (HPC). 

Altair Data Analytics enables people of all skill levels to access, generate, and use smart data to make insightful, informed decisions.

Our no-code data transformation, AI/ML, and data visualization tools reduce the complexities often encountered in data analytics. We eliminate the need for specialized programming knowledge and democratize the analytics process. As a result, people across the business can leverage the value of insightful analytics. 

Visit www.altair.com/data-analytics to learn more!

(PREVIOUSLY RECORDED)
Manufacturers are increasingly relying on data analytics to optimize operations, and for good reason. Manufacturing environments are data-rich. Data from sensors, machine logs, and production records can be used to extract valuable patterns and insights, which can help avoid downtime, reduce scrap, and improve sustainability. Implementing data analytics is an essential part of the digital transformation journey and more attainable than you may think.

Watch this webinar with ABI Research and Altair to:
 - Determine what factors to consider when evaluating manufacturing data analytics vendors 
 - Learn about Altair RapidMiner, ABI’s top-ranked data analytics and AI platform for manufacturing analytics 
 - Get inspired by real-life manufacturing use cases and successful deployments
 - See how to maximize the value of your data and compete well into the future.

Webinar: Unlocking Manufacturing's Data Potential with AI and Analytics

Welcome to this demonstration video of the Altair RapidMiner platform, our end-to-end AI solution for all users in your enterprise.
Altair RapidMiner is a platform in which data science, machine learning, AI and traditional data analytics converge.
There are close connections to Altair's other two platforms for simulation and high-performance computing. But today's demonstration will mainly focus on Altair RapidMiner.

Agenda:
- Planning and Design
- Data Preparation
- Data Exploration and Visualization
- Data Science Techniques and Machine Learning
- Generative AI
- Notebooks and IDEs
- Model Consumption
- Project Progress
- Key Take Aways

Altair RapidMiner Demonstration

Businesses across all sectors are facing significant challenges when it comes to modernizing their data analytics stack.

Terms like 'data transformation' and 'infrastructure optimization' are always used, but what does that mean? How do the people on the ground, like you, even begin to address how to approach the vast and nuanced projects related to modernization?

The most pressing question we hear from our customers is: How can we maintain investments made in legacy systems, like those created using SAS, and leverage modern platforms that offer new benefits and address issues like staff skills, open-source deployments, flexibility, and transition to the Cloud?

In this 30-minute webinar, our experts will discuss:

How to identify factors that impact reliability when looking at sizeable enterprise-wide modernization projects
Real-life successful deployments and why our phased approach works for large organizations
How you can leverage new Generative AI technologies safely and effectively
How our technologies can help you build your unique competitive advantage without having to throw away investments made in legacy structures and systems
How to accelerate your journey to the cloud

Clear Path to Modernization: Future-Proofing Your Data Analytics Infrastructure

With the emergence of ChatGPT we have reached another pivotal moment with AI. At the very least, it has unlocked a next-level fever of interest in AI and potentially the ability to disrupt whole industries. This has arguably most organizational leaders wondering, how might this impact my business. In this panel discussion, we will explore the potential impact on manufacturers.

Panelists:
Naresh R. Jasotani I AI/ML Innovations Lead-Automotives, US I Google Cloud
Dr. Ingo Mierswa I Senior Vice President, Product Development I Altair
Mark Do Couto I Senior Vice President, Data Analytics I Altair
Mahshid Shirani I Data Science Manager I Altair

Changing the Pace of Manufacturing Transformation through Generative AI & LLM

This video demonstrates how the Altair team utilized our solutions to identify characteristics that lead to a higher risk of flood and ways to help mitigate the displacement and loss of life due to these events.

We’ll show how we used the Altair RapidMiner platform to:

Get to insights faster by enabling domain and data experts to collaborate seamlessly
Leverage existing SAS language and Python code in a multi-language environment
Deliver flood risk warnings, ahead of time, to the right stakeholders with a real-time dashboard

Data preparation for income and expense shock

Visualize and validate credit risk models

Visualize and validate credit risk models : Episode 3

We understand safeguarding your portfolio from current economic shocks is your top priority. That means you require a throughout data-driven approach for credit-risk assessment. Then your credit-decision model should be the reflection of the same and needs to be continually fined tuned. Altair with Deep Credit Risk brings Credit Risk Trends and Analytics, a three-part webinar series on credit risk analytics with a focus on the pain points of developing models that reflect the driving economics.

In part one webinar, we understood the challenges in the credit risk industry. Build an understanding of the role of liquidity, equity, and many other key banking features. You learned to do feature engineering and efficient selection, for instance, the impact of income and expense shocks on loan repayments. You were able to pre-process credit information, import information, and merge data from various sources.

Now, with the right data, in this part-two webinar, you will learn to build credit risk models through machine learning techniques and understand the impact of the economic downturn on the model outcome. You will develop a good understanding of the pitfalls of machine learning and the merits relative to econometric regression techniques. You will be able to predict defaults, payoffs, loss rates, and exposures. You will be able to efficiently validate credit risk models. We will compare and interpret various validation strategies and learn how to build meaningful user interfaces, compare outputs for models in terms of stability, discrimination, and calibration and efficiently communicate outputs. This will give you the confidence to build credit models accurately as we cover deep domain knowledge on-

Key Discussion Points:
§ Non-Linearities of risk drivers
§ Impact on Illiquidity and Leverage
§ Machine Learning Models
§ Bias-Variance trade-off
§ Predictability and pain-points
§ Showcase of lender internal data

Credit Risk Modeling Using Machine Learning : Episode 2

Martha Bennett I VP, Principal Analyst, Forrester
In this presentation, Martha Bennett discusses:
•How leading firms are using data & analytics to reduce risk
•What impending and potential AI regulations mean for banks and insurers
•How a solid foundation of data and analytics is the key to agility and innovation

Be Future Fit With Data And Analytics

Credit Risk Trends and Analytics
Part 1 - Data preparation for income and expense shocks

We understand safeguarding your portfolio from current economic shocks is your top priority. That means you require a throughout data-driven approach for credit-risk assessment. Then your credit-decision model should be the reflection of the same and needs to be continually fined tuned. Altair with Deep Credit Risk brings Credit Risk Trends and Analytics, a three-part webinar series on credit risk analytics with a focus on the pain points of developing models that reflect the driving economics.

In this webinar, we will try to understand the challenges in the credit risk industry. Build an understanding of the role of liquidity, equity, and many other key banking features. You will learn to do feature engineering and efficient selection for instance the impact of income and expense shocks on loan repayments.
You will be able to pre-process credit information, import information, and merge data from various sources. This will give you the confidence to build credit models accurately as we cover deep domain knowledge on-
Impact of Income and Expense Shocks on Loan Default Risk
Impact on Illiquidity and Leverage
External Data
Default Rate Cycles
Predictability and Pain Points
Showcase for Lender-internal and External Income Data

We will be showcasing-
Pre-processing of mortgage loans
Importing of external income data
Merging
Modelling
Visualization
Descriptive statistics
Regression

Credit Risk Trends and Analytics : Episode 1

AI can do some fascinating things like write code and movie scripts for us. But the AI honeymoon is over and many organizations are still far from the dream enterprise. The reality is most invest in AI and there’s very little to show for it. The reason? Friction: organizational, technical, and financial.

Dr. Ingo Mierswa, Senior Vice President of Product Development at Altair, shared how Altair + RapidMiner removes the friction between people, data, and the business impact AI should have. Attend this session to learn how Altair RapidMiner:
- Accelerates users from concept to production by offering AutoML, visual workflows, and coding for all aspects of data science in a single, seamlessly integrated platform
- Modernizes established data analytics environments such as SAS and Python
- Caters to enterprise needs in a seamless, future-proof way with desktop and cloud offerings

Altair Expert: Altair + RapidMiner Means Frictionless AI

Ravi Parmeswar delivers expert advice on how to drive extraordinary customer experiences and build high-performance organizations through data and analytics.

Using Data Analytics to Improve the Customer Experience

Mieke combines her industry experience at imec with her research experience at the Vlerick Business School to deliver an excellent perspective on how to transition to a healthier, greener, and more sustainable future using AI and data. In this episode, she speaks about
energy efficiency, streamlined collaboration, and explainable methodologies.

Future Says - Geertrui Mieke De Ketelaere

Uncover business use cases and common implementation journeys, including:
- Successfully integrating SAS language programs into teams with users of varying skill sets, leveraging drag-and-drop coding or Open Source languages (R, Python, etc.)
- Governing and centralizing access and credentials for siloed data programs and workflows
- Deploying end-to-end analytics for on-demand analysis, scheduling, pipelining, and more
- Help teams using different technologies to collaborate and make the best use of all available technologies
- Understand how your team can take action to reduce costs and drive data workflow efficiencies

Deploying an Alternative SAS Language Environment Part 2

An Introduction to Altair SLC™ - a SAS language compiler and execution environment, Altair Analytics Workbench™, and Altair SmartWorks Hub™
- Identify your team’s data needs and gaps currently preventing seamless, end-to-end analytics.
- Find out how other organizations switched to a strategic modern platform, saved costs and resources, and preserved existing SAS language investments.
- Learn about how SAS language program capabilities fit into Altair Data Analytics.

Deploying an Alternative SAS Language Environment Part 1

Vijay presents an update on digital twin adoption to date. He talks about the potential as well as the challenges and advocates for a ‘think big, start small, fail quickly, scale fast’ approach. Vijay believes in the critical role that agile methodologies and cultural shifts play in delivering this next-generation innovation.

Creating the Identical Digital Twin

Altair's Chief Technical Officer, Sam Mahalingam, discusses AI adoption during Altair's Future.AI virtual event.

Rise of Edge - Enterprise AI Adoption (EMEA)

Fraud mitigation requires technologies and business processes that are flexible in their construct, easily understood by all who are involved in fraud prevention, and are agile enough to adapt to new attacks without needing to be rebuilt from scratch.

During this webinar, our experts explore:

● Various data transformation models that focus on fraud detection (i.e. Digit tests, Relative Size Factor test, and others)
● Automation of data workflows to capture anomalous behavior
● Creation of flexible, understandable, and agile business rules using ML
● Machine learning capabilities for fast visual anomaly detection

Financial Fraud Mitigation with Data Analytics

Data Analytics

Machine Learning

Financial Fraud

Fraud Analytics

Fraud Detection

Fraud Prevention

Fraud Management

Financial Analytics

Anomaly Detection

Mitigate Data Risk

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Financial Fraud Mitigation with Data Analytics

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