Artificial Intelligence in the Enterprise Software Gets Smarter Artificial Intelligence in the Enterprise Dr Kaustubh Chokshi CEO, Intelligent Business Systems

Cutting-edge Artificial Intelligence techniques are ensuring that enterprise software today can dynamically adapt to rapidly changing business environments and provide realistic decision support and trend forecasting for enterprise managers to act on ******* AI-based software dynamically learns from experience and adapts to the environment, as it is equipped to acquire knowledge from the data generated within the organization, as well as from expert opinion and external data sources

Artificial Neural Network Bayesian Statistics Genetic Algorithm Artificial Immune System

Artificial Neural Network (ANN) Process info in a manner similar to biological nervous systems such as the human brain. Large number of highly interconnected processing elements. ANNs learn by example, much like humans. An ANN is initially “trained” with large amounts of data and rules about data relationships. Once trained, a neural network becomes an “expert” in its specific area of operation and can offer projections and trends based on past data and answer “what if” queries.

Artificial Neural Network (cont.) ANNs operate using several different techniques, including gradient-based training, fuzzy logic, genetic algorithms and Bayesian methods. ANNs can derive meaning from complex or imprecise data, thus recognising patterns and determining trends that humans or other computer techniques would most likely fail to notice. Neural networks can also learn temporal (time-sensitive) concepts and can thus be used in signal processing and time series analysis.

Bayesian Statistics Bayesian Statistics enables calculation of the probability of a new event on the basis of earlier probability estimates of an event or events in the past, derived from existing empiric data. According to Bayesian logic, the only way to quantify a situation with an uncertain outcome is through determining its probability. Thus, the knowledge of prior events is used to predict future events.

Bayesian Statistics (cont.) This is an iterative or learning process and is the preferred method for designing software that learns from experience. Pattern recognition is based on Bayesian inference, and this forms the basis of varied applications such as spam detection, fraud detection, intelligent search, unstructured text mining, etc.

Genetic Algorithm A genetic algorithm (GA) is an algorithmic model that tests a set of results, each represented by a string, and selects the best fit among them. In this methodology, of a number of possible programs or functions within a program, only the most effective survive and compete or cross-breed with other programs, with the intention of evolving into an ever-better solution to a particular problem. Used to find approximate solutions to difficult-to-solve problems.

Artificial Immune System (AIS) Like other biologically inspired techniques, AIS tries to extract ideas from a natural system, in particular the vertebrate immune system, in order to develop computational tools for solving engineering problems. Used for pattern recognition, data analysis, data clustering, function approximation and optimisation.

Linear "linear" in its scientific sense; that is to say, as implying parallelism between the magnitude of a cause and the magnitude of its effect.

One of the characteristics of today’s business environment is that it is definitely non-linear. Non-linear systems exhibit unpredictable but non-random cause-and-effect relationships. Edward Lorenz' "butterfly effect" provided an illustration of this behaviour by postulating that a butterfly flapping its wings in Brazil might cause a tornado in Kansas. In complex systems, even a very small change in initial conditions can rapidly lead to changes in the behaviour of the system that appear counter-intuitive in both nature and magnitude.

Decision Management using AI is a systematic approach to automating and improving decisions across the enterprise. Businesses using AI gain much greater control over the results from high-volume operational decisions. AI-based Decision Support Systems aim to increase the precision, consistency and agility of these decisions while reducing the time taken to decide and the cost of the decision.

These decisions are typically those that an organisation uses to manage its interactions with customers, employees and suppliers. Computerisation has changed the way you approach decision-making by enabling decisions to be based on historical data, prior decisions and their outcomes, corporate policies and regulations.

Customer Classification Data completeness Data Division Data normalization Data cleaning Data sampling Analysis Bayesian Network Neural Network Decision making Customer Classification Prediction Tourism Dataset

Operational business decisions - those taken in large volume, every day. They are differentiated from "strategic" decisions such as where to open a new store or when to drop a product line that are rarely the same twice and that simply do not happen that often. Clearly these are important, needs to be automated and make them in "real-time". Many examples, such as approve/decline, next-best-offer to make a customer, authorisation of a sale, fraud detection in a claim, account application processing, etc. These "tactical" decisions determine the way in which you will manage processes and customers such as decisions about which segments of a customer base will receive which precise offer.

Alerts - alerting the user to a decision-making opportunity or challenge. Problem recognition - identifying problems that need to be solved as part of the decision-making process. Problem solving - providing and evaluating alternative and/or complementary solutions. Facilitating/extending the processing of knowledge - overcoming some of the human limitations of the speed and volume of information that can be processed (e.g. acquisition, transformation, exploration). Stimulation - stimulating the human perception, imagination, or creating insight. Coordinating/facilitating interactions - in multi-participant decision making. Various other stages and activities in the decision-making process.

Is a concept developed by Richard Hackathorn. Decision Latency is the time it takes to receive an alert, review the analysis, decide what action is required, if any, based on knowledge of the business, and take action. Operational decisions require very low decision latency. Overall Aim of a Decision Management System is to try and reduce Decision Latency.

Precision. Increase revenues and improve risk management through greater segmentation, more relevant offers and better risk management. Benefits include: Higher revenue yield per customer interaction, through better targeting and segmentation and through more timely responses to customers Lower losses from fraud and bad debt, through using analytics to improve risk management   Lower costs through refined targeting, such as eliminating "off target" marketing messages or prospects that are unlikely to buy

Consistency. Ensure that all decisions meet your rules, policies and regulations, by automating 75% or more of your operational decisions. Benefits include: Lower costs of making decisions through automation, reducing the number of people and streamlining the processes needed to make or process a decision. Lower costs of compliance, regulatory requirements, through centralised and easy to update business rules management. Faster decisions that operate at the speed of the transaction, lowering hand-off costs between systems and between people.

Agility. Meet new competitive and compliance demands by rapidly changing your business rules, and instantly executing new strategies. Benefits include: Improved strategic alignment, greater competitiveness through faster response to market changes and regulatory demands. Greater return on new product & market opportunities, through faster time to implement and change decision-based processes, change approaches to the market.

Increase revenue and profitability by improving the consistency, relevance, speed and precision of customer decisions, getting more value from every customer interaction. Improve customer relationships and retention through more targeted offers, faster response to service requests and more consistent treatment. Minimise losses through the use of analytics for more accurate and consistent risk assessment and fraud detection. Gain competitive advantage by being more nimble than the competition -- get new strategic initiatives, products, campaigns and pricing to market faster and with greater precision and consistency. Ensure and demonstrate rigorous compliance with corporate and regulatory policies.

Reduce the costs associated with decision-based processes, while improving decision consistency, speed and quality. Leverage existing investments in data warehousing and CRM—derive more value from all corporate and external data sources. Reduce ongoing maintenance costs required to change / tune models, rules or strategies that are in production.

AI can be applied to virtually any business area that involves high-volume, operational decisions, or the use of analytics and business rules to improve decision strategies. Customer acquisition and retention Matching prospects and customers with product/service offerings Core customer decisions-underwriting, channel selection, credit, pricing, etc. Forms and data management Work process control Fraud detection Claims management Guidance and employee support Customer response and service Debt collection and recovery Agency management Network integrity assurance Online recommendations Product configuration and design Regulatory compliance

The supply chain of an Enterprise includes the network of all suppliers and activities involved in the process of transforming the requisite raw materials into finished goods and delivering them to customers. The objective of supply chain management (SCM) is the integration and optimisation of all the components and processes involved.

The purpose of AI here is to evaluate the different options of transporting the different products to different terminals based on demand forecasts and forward prices so that the margins are maximised. The economic optimisation is accomplished by using the Linear Programming technique. The margins calculated include sales revenues as well as the costs of purchase, production, inventory holding, transportation and materials handling. AI plays a key role in Distribution Network by not only making the best use of capacities in the system (asset utilisation), but also ensures that all forecast demands are met (prioritises the distribution to make the most economic sense).

Product Customer Fit: which means marketing a product to a customer who most desires/needs/uses it. Demographics Profiling: Information such as age, gender, household size and parental status offer the most basic understanding of who the customer is. Geo-Demographic Profiling: Location and type of area in which people live. Psychographic Profiling: Attitudes, Values, Motivations and Aspirations. Customer Loyalty: Value, Frequency and recurrence of purchases. Behaviour Profiling: Profiling based on Consumer Behaviour focuses on issues key to anyone seeking to sell or market products: what do your users buy? What are they in-market for? How much do they spend, and where do they spend it?

AI can help find answers to key questions: How can we know which specific marketing actions to take, based on purchase behaviour and personal profile information, to maximise value? How can we mine the data to derive actionable insights into customer segments and response patterns? How can we spend our marketing efforts more effectively, and minimise waste? How can we create the messages and offers that are most likely to elicit a favourable response without doing expensive in-market testing? How can we establish an ongoing dialogue and a deeper level of intimacy with our best customers? How can we measure the sales and profit resulting from our investments in data-driven marketing programs? How can we integrate customer data from all databases, channels and touch points to create a 360-degree view of our customer relationships? How can we determine which customers account for the vast majority of our profits (and future profit potential) and how can we map their “genetic makeup” so that we can then market to others just like them?

Behaviour based profiling, behaviour, and for anybody concerned about what their customers are doing What AI provides is the ability to market your products to the right customer depending on his buying patterns. AI-based system can also encode expert opinion, for instance, what kind of red wine will go with what kind of blue cheese. This helps to promote other lines of needed products to relevant customers. Also, the system has the ability to “learn” from data about customers’ preferences with respect to wine and blue cheese. Profiling using AI can then bring objective information to the marketing department. This can be used to reduce the cost of the campaigns by selecting only the prospects that have a high probability to reply positively, or they can be exploited for fraud detection. In a nutshell, AI-based customer profiling and advertising integrates AI/CI platforms needed to perform behaviour analysis, context-sensitive acquisition, cross-selling and retention programs, enabling multi-product, multi-channel companies to drive more efficient and profitable customer interactions.

Detect every single event fraud as it occurs. Detect fraud trends more quickly. Minimise the cost of manual labour. Convert more valid orders easily. Minimise the cost of customer service inquiry resulting from valid order rejection. Control fraud risk tolerance. Detect known and unknown ways of frauds.

Time Series Data Analysis (NN): Numeric Structure Text Merchant Analysis Card Holder Profile Transaction Data Time Series Data Analysis (NN): Numeric Structure Text Unstructured Text External Data Known Behviour Detection Unknown Behviour Detection Experts Inferences Past Experiences Output: Risk Scores Explanations Decisions External Rules

AI’s analytical power answers "What next?” AI explores and uses data patterns to make forward-looking predictions, or to make complex statements about customers by evaluating multiple data patterns. AI uses the patterns those represent in the enterprise, in order to "formalise" the relationships and predict future behavior consistently. Data Mining explores Data mining applies sophisticated mathematics and/or AI to data in order to search for useful patterns in large data sets. Data mining is often one stage in developing an AI-based system.

BI delivers insight, predictive analytics delivers action Traditional business intelligence (BI) tools extract relevant data in a structured way, aggregate it and present it in formats such as dashboards and reports. BI helps businesses understand business performance and trends. BI focuses on past performance, predictive analytics forecasts behaviours and results in order to guide specific decisions. BI suites now include some analytics. However, BI analytics almost always aggregates past customer data in a collective sense - for example, how many of my customers are in a particular set so I can forecast product sales by quarter?

BI tools are more exploratory than action-oriented. Exploration is more likely driven by a business user than an analyst. AI can help BI to focus on past performance predictive analytics forecasting behaviour Results/scenarios in order to guide specific decisions. If BI tells you what’s happened, AI tells you what to do. AI in BI is important in order to make better business decisions.

And finally… About Intelligent Business Systems Intelligent Business Systems (IBS) provides innovative business solutions incorporating cutting-edge Artificial Intelligence (AI) engines. The company’s core competence lies in Artificial Intelligence, with a significant focus on Business Intelligence. IBS also has expertise in Robotics and Bioinformatics. IBS has a very strong research focus in everything it does Established in the UK in 2003, IBS has just expanded into India in a very big way Check out the interactive presentation on the CD to learn more! www.intelligentsystems.biz

Dr Kaustubh Chokshi kaustubh.chokshi@intelligentsystems.biz Thank You! Dr Kaustubh Chokshi kaustubh.chokshi@intelligentsystems.biz