Software Gets Smarter

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Software Gets Smarter

• Artificial Intelligence in the Enterprise
• Dr Kaustubh Chokshi
• CEO, Intelligent Business Systems

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• 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

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• Artificial Neural Network
• Bayesian Statistics
• Genetic Algorithm
• Artificial Immune System

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• 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.

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• 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.

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• 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.

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• 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.

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• 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.

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• 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.

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• 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.

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• One of the characteristics of todays 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.

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• 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.

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• 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.

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• 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.

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• 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.

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• 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.

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• 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

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• 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.

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• 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.

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• 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.

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• Reduce the costs associated with decision-based
  processes, while improving decision consistency,
  speed and quality.
• Leverage existing investments in data warehousing
  and CRMderive 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.

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• 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

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• 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.

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• 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).

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• 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?

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• 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?

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• 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.

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• 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.

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• AIs 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.

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• 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?

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• 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 whats happened, AI tells you
  what to do.
• AI in BI is important in order to make better
  business decisions.

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And finally

• About Intelligent Business Systems
• Intelligent Business Systems (IBS) provides
  innovative business solutions incorporating
  cutting-edge Artificial Intelligence (AI)
  engines. The companys 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

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Thank You!

• Dr Kaustubh Chokshi
• kaustubh.chokshi_at_intelligentsystems.biz

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