Agenda

Bayesian methods have long been known as a powerful weapon in the fight to manage risks of all kinds. Nevertheless, these methods have long remained a niche tool, used only by a handful of sophisticated practitioner in the field.

New developments in Artificial Intelligence and IOT have rejuvenated these methods, and have brought them to the fore as a leading method of modelling and modelling complex systemic risk – including the difficult task of extrapolating from today’s data to identify future black swan risk events.

Come and learn the basics of the Bayesian approach and see how a new generation of AI tools makes Bayes easier and more powerful to work with than ever before.

JAMES WARD
Managing Director
Epistemology

B) InsurTech Roundtable

C) Fintech Roundtable

D) RegTech Roundtable

E) Fraud/AML and Roundtable

CLAIRE VINES
Principal-Head of Technology
Iexvoco

• AI’s Bigger Ability in Handling and Evaluating Unstructured vs Structured Data
• How Machine Learning Can Improve Risk Segmentation, Evaluation and Detection
• Ability of AI to Use Advanced Algorithms to Analyze Text in order to Derive Insights and Social Media Sentiment from Unstructured Data

• How to Embed Deep Learning in Your Organization’s Existing Technology Development Life-Cycle
• Key Mechanism of Developing Your Machine Learning System Design – the Model, the Parameters and the Learner
• Devising a Successful Machine Learning Strategy

• What questions you should be asking throughout the AI implementation
• How you can add value in your role as part of the AI implementation
• What risk, compliance and regulatory factors to consider through each stage of the AI implementation

• Impact of AI in Risk Modeling
• Types of Structured and Unstructured Data Used in AI, Machine Learning and Advanced Analytics for Risk Modeling
• Supervised vs Unsupervised Learning
• How to Assess Effectiveness of the Model

• How Big Data and Machine Learning Can Be Used to Improve Underwriting
• How AI Can Improve Claims Processing and Help to Spot Fraud
• Leveraging on Existing Data and Analytics to Generate Deeper Risk Insights
• Automating Underwriting Processes

• Global payment digitalisation
• Cybercrime / sophisticated fraud in digital world
• Conventional methodology for fraud management vs new age technology
• Real time fraud detection is a key and usage of ML or AI for proactive risk management
• Ecosystem / integrated risk management
• Collaboration / cooperation

• Why should we use this technology
• Main components that contribute to an automated solutions
• Approach & Evolution of processes
• What are the benefits?

• Reviewing Challenges and Lessons Learnt from Existing AI Projects
• How Can We Accelerate AI Implementation in a Sustainable Manner?

CLAIRE VINES
Principal-Head of Technology
Iexvoco

• Case studies highlighting real-life examples where AI is used for fraud detection and risk management
• Emerging trends in AI-related fraud risks for insurers to consider

• What Are the Acceptable Failure Rate of an AI Fraud Detection?
• Can AI Replace the Human Touch and Gain Trust on Client?
• What Infrastructure You Need
• Challenges of Forming the Team: How to Attract Talent
• Synergy or Conflict in the Team
• Mixing the Old and New

• RegTech imperative: insights from the Royal Commission and consumer expectations of Financial Services organisations.
• RegTech landscape: with Aust being a top 3 and an emerging centre of excellence
• Collaborating with RegTechs – how RegTech’s and use of AI can help enterprises achieve better compliance and regulatory risk detection, assess risk exposure and anticipate future threats
• Future-of-work transition advantages from RegTech for organisations

• How Ailira Uses Natural Language Processing (NLP) to Help Law and Tax Professionals to Research in Seconds
• How Ailira Automates Its Legal Services to Assist Victims of Domestic Violence by Providing Legal Advice and Automate Risk Assessments and Documents
• Discover How Ailira Could Replace Tax Agents in Future by Answering Your Complex Tax Questions on Superannuation, Capital Gain Tax and Other Tax Issues

• Business is really all about risk management.
• Bayesian methods have long been known as a powerful weapon in the fight to manage risks of all kinds. Nevertheless, these methods have long remained a niche tool, used only by a handful of sophisticated practitioners in the field.
• New developments in Artificial Intelligence and IOT have rejuvenated these methods, and have brought them to the fore as a leading method of modelling and modelling complex systemic risk – including the difficult task of extrapolating from today’s data to identify future black swan risk events.

• Sharing on Different Industries’ AI Deployment on Boosting Their Risk Management and Fraud Prevention Function
• Achievements and Benefits That Are Derived from AI Deployment
• Challenges and Implications