ABSTRACT. What if you could reduce your planning process from one week to one hour, or from one hour to one second? What if you could, at the click of a button, improve your bottom line by double digits? In this session, you will learn to do just that by leveraging machine learning (ML) and Decision Optimization (DO) technologies together. You will learn the differences and complementary strengths of ML and DO, learn about best practices, and see examples of combining these technologies to achieve financial gains and efficiencies. The session includes demos covering use cases such as marketing campaign planning and predictive maintenance.

ABSTRACT. Boxever have been providing 1:1 customer personalisation capabilities to some of the worlds top airlines and financial services businesses for over 6 years. Engage our customer engagement engine was built on Drools and had a coding interface which allowed coding of rules in a user friendly domain specific language created by Boxever to provide real time personalisation As our customers matured however they demanded the ability to arbitrate between marketing messages, service messages and customers service messages. They also were seeking ways to apply AI and machine learning across all customer touch points. This case study will discuss the challenges we faced both from a technical and user experience perspective in creating a decisioning capability based on the DMN standard for first best action and next best action and how these were overcome. The key takeaways will be: Building the right DMN modeller for enterprise decisioning – augmenting the DMN standard to improve flexibility Choosing the right scalable architecture to support multi-tenant enterprise deployment Decisioning and collections - To contain or not to contain Testing Decision Models - Component, Model and Variant Analytics - Choosing the right KPI's Challenges of building optimisation workflows (A/B/N) into model deployment

Audience: Business/Technical

Speakers:

Roy Robinson Director of Product, Boxever Roy has spent the past 10 years as a product specialist working with SaaS businesses to build out data and analytics platforms. He has a wealth of experience in building scalable real time decisioning products which leverage the latest data science techniques in both the security and customer experience fields. Roy has a passion for user experience and practical applications of AI. Roy has a Masters in Mathematics and Computer Science from Queens University, Belfast and is a SciFi and Fantasy novel fanatic!

Alan Giles CTO, Boxever Alan is a Co-Founder and the CTO at Boxever. He is a highly accomplished software technologist, software visionary, and entrepreneur with over 14 years’ experience leading, developing and delivering highly scalable and fault tolerant distributed systems. He is passionate about large-scale systems, cloud security, and building new architectures to support petabyte-scale data processing. In addition to developing world-class technology for the travel industry, Alan is a mentor and advisor in the Dublin startup scene and you can even find him occasionally at an all-night hackathon.

ABSTRACT. In 2014 we introduced the "The Decision Model" method by B. v. Halle and L. Goldberg to our business analysts and launched the OpenRules rule engine in IT. At the same time, we could win a first project to use both the method and the new rule engine for the implementation of underwriting decisions in the individual life insurance. In 2017 we switched from "The Decision Model" to DMN and introduced the tool Innovator for Decision Modeling. After a total of more than four years of experience in decision modeling, we should have developed hundreds of Decisions by now. In fact, to date there have been about 4,000 rules in 54 decisions. What were the reasons why we haven't already turned all business logic into decisions? Many requirements must be met for the use of decisions to be accepted in both business and IT. The business must be ready to take responsibility for the business logic again and to manage it. Business analysts must be affine with the decision method and have appropriate tools to support them in their work. IT must be ready to leave the business logic back to the business. However, there are already many business applications that contain their own rule components and in which the business logic is partly maintained by IT. The transition from the modeled decision in Business to the executable decision in IT must take place without additional effort if possible. For this purpose, we have implemented a model-driven approach from the outset. And finally, the tools used in business and IT must not require too high investments, at least at the beginning. This presentation describes, • how we introduced a method for the presentation of rules in Business for the first time with "The Decision Model" and started with a cost-effective solution in initial projects, • switched to the OMG standard DMN in 2017 and used Innovator for decision modeling in Business, • which hurdles are to be expected when using Decisions.

Key take-away: Challenges in application of decision models in Business and IT Audience: business, new Industry Sector: Insurance Key Technology: DMN, Innovator, OpenRules

ABSTRACT. Waiting times for patients in Irish hospitals have grown in recent years to unprecedented levels, at the beginning of 2018 there were just over 500,000 people waiting to see a specialist for an outpatient appointment, of a total population of 4.77 million in Ireland. We investigate the use of data analytics and optimization to understand the current situation, predict future short and medium term demand, and study the impact of changes in processes and resource levels. While overall resource levels are clearly inadequate, we also study the impact of did-not-attend no-shows, the possibility of overbooking, and of load distribution across clinics and hospitals. Some of the analysis is based on publicly available data, with results made available in at https://patientwaitlistanalysis.wordpress.com/

ABSTRACT. Product Rule Systems are well known for being at the heart of Decision Magement, however the paradigm, with all its pitfalls, has not changed that much over the decades. They typically offer a monolithic design with a single working memory and rulebase, with limited rule orchestration and very quickly hit the limitations with rule chaining complexity. Approaches like DMN take a different strategy and simplify things by reducing the capabilities down to those needed for the specific types of problems DMN aims to solve. However there still remains a need for more powerful systems too, but they must modernise if they are to retain industry relevance.

This talk will discuss the ongoing innovations that are helping to modernise Product Rule Systems, while keep within the context of the Drools rule engine. Those innovations involve new techniques for rule modularisation, rule orchestration, recursion control and argumentation as well as breaking away from the traditional monolithic design of a single working memory and rule base.

ABSTRACT. The Climate Change Act and Social Value Act in the UK place responsibilities on many private sector and all public sector organisations to consider not only the value to customers and stakeholders of their activities but also the effect on greenhouse gases and the society they operate within.

The healthcare sector has a particularly significant place in this work, forming as it does a large part of the public sector and being present in every part of UK society. This programme that we've been involved with for some years now aims to recruit that considerable geographic & economic muscle to positively drive social and environmental action.

The programme collates the best and most up to date climate and social value modelling available and applies it to the problem of calculating a complete Sustainability report for each and every hospital trust and clinical commissioning group (CCGS - who commission primary healthcare providers) in England. In numerical terms this deals with almost 500 data inputs for each of around 200 hospital trusts, a further 200 CCGs as well as Ambulance Trusts, Mental Health providers and so on.

This case study will show how DMN has been used to document the previously black box system allowing far more people to understand, critique and improve the reporting process. Furthermore by making these decision models executable we have dramatically reduced the complexity of maintaining and updating the system as new and updated data becomes available.

ABSTRACT. The financial services industry has long been a driver for technology innovation. Volatile regulations, increased demands for compliance, and a requirement for transparency necessitate the ability to quickly create and easily manage decisions across systems. The evolution of disruptive “FinTech” systems has attempted to address some of these needs. However, those requirements coupled with a constant demand for faster online processes has now created the need for Regulatory Technology or “RegTech”. A key component of new systems will certainly be the ability to create services or disseminate decisions in a consistent, unambiguous and transparent fashion.

The Mortgage Industry Standards and Maintenance Organization (MISMO) strives to allow participants in the mortgage industry (mortgage lenders, investors, servicers, industry vendors, borrowers) to exchange information and do so more securely, efficiently and economically. Although primarily focusing on an XML-based data standard thus far, they now seek to enable seamless exchange of all information between mortgage industry partners. To that end, MISMO is in the final stages of officially endorsing DMN as the recommended decision modeling standard for the mortgage industry.

This presentation will focus on the integration of business standards in the mortgage industry (MISMO) with technology standards (DMN, BPMN) to enable a powerful approach to handling FinTech and RegTech. These approaches will be demonstrated with their application to new mortgage requirements and regulations around the Uniform Residential Loan Application (URLA) and eMortgage. In addition, a new set of industry players will be empowered to succeed by having their compliance burden eased.