2. Introduction
Artificial intelligence (AI) concerns the study and development
of intelligent machines and software. The focal problems include
the development of software that can reason, gather
knowledge, find patterns, generalize, discover, associate, plan
intelligently, learn, communicate, adapt to new situations.
AI allows users of data to automate and enhance complex
descriptive, predictive and prescriptive analytical tasks that,
when performed by humans, would be extremely labour
intensive and time consuming. Thus, AI can have a significant
impact on the role data plays in deciding how we work and how
we conduct business.
Companies as well as public bodies seek to enhance their
competitive advantage by better understanding ever-growing
amounts of data. AI offers the technology and methodology to
do so, which is why the market for AI-based tools and
applications is growing rapidly.
Trends
Artificial intelligence can find use in many different sectors. In
any industry that deals with large amounts of data, techniques
and technologies based on artificial intelligence can be of value.
AI solutions are by nature multidisciplinary, involving computer
science, mathematics, statistics and philosophical thinking. It is
the basis for software that supports, facilitates and improves
analytics and decision making.
In the last years a significant progress has been achieved by
solving complex business processes’ analysis, optimization and
control problems through the using of various methods of
artificial intelligence. These methods include artificial neural
networks, fuzzy systems, multi-agent systems, evolutionary
computing, swarm intelligence and the virtual plant paradigm.
While individual models of biological and natural intelligence
have been applied successfully to solve real-world problems, the
current trend in artificial intelligence is to develop hybrids of
already known paradigms combining various intelligence
AI is the basis for software
that supports, facilitates
and improves analytics
and decision making.
3. techniques, since no one paradigm is superior to the others in
all situations. In doing so, we can use respective strengths of the
chosen algorithms or the models and eliminate weakness of
some other individual components.
Solving the Optimization Problem
Unfortunately, because of relatively complicated tools,
intelligent systems have only limited application examples in
improving of real business processes today, even though they
have been already very successfully used for various engineering
applications.
Our proposed general application schema of artificial
intelligence algorithms and methods is an attempt to
bring the intelligent systems’ methodology, techniques
and tools to the business community. Solving practical
tasks of business processes’ analysis, optimization and
control.
The following general schema includes all the innovative
methods and algorithms of artificial intelligence, which are
necessary for business processes optimization.
Figure 1. General application schema of AI methods and
algorithms for business processes optimization
4. The data and information flows from real or simulated processes
are collected using advanced multi-agent systems. This
information is used to create predictive models of the process.
Predictive models are used to find optimal decisions and actions
for business’ process optimization. Because of various process
constraints and complicated process models, advanced
optimization tools are involved here to find the best decisions
and problem oriented solutions.
“Virtual Business” tools included in the general schema allow the
users to test various process optimization and analysis
algorithms and “IF… THEN” scenarios, before they are making
costly and risky experiments with actual business processes.
For on-line monitoring and analysis of business process data, as
well as for detection of possible faults and unexpected behavior
of the process general application schema includes special tools
for intelligent data analysis. Because of high computation load it
is suggested for the companies to realize most computation
tasks of business optimization using cloud computing approach.
Cloud computing is a technology that uses Internet and powerful
remote servers to obtain business processes optimization
solution. This technology allows for much more efficient and
user-friendly computing by centralizing storage, memory,
processing and bandwidth.
The most important parts of this general application schema
are:
Intelligent predictive models
Intelligent optimization tools
Intelligent analysis tools
1. Intelligent predictive models
Today’s business world is driven by customer demand.
Unfortunately, the patterns of demand vary considerably from
period to period. This is why it can be so challenging to develop
accurate forecasts. Forecasting is the process of estimating
future events, and it is fundamental to all aspects of
management.
5. The new generation of AI technologies help improve the
forecasting process including such applications as product
demand, employee turnover, cash flow, distribution
requirements, inventory, manpower forecasting etc.
The proposed general application schema involves an innovative
approach for building business process models. We called it
“Hybrid Predictive Modeling” technique and it is based on
combination of fundamental models, operator knowledge and
data driven models.
“Hybrid Predictive Modeling” technique
Business process optimization requires a lot of knowledge about
the business processes. The classical way of representing
business process knowledge in science and engineering is to
use mathematical models based on first principles. This requires
a thorough understanding of mechanisms dominating the
business process dynamics. However, many essential details of
the various business processes are not yet so well understood in
order to construct well working fundamental mathematical
models. Hence, to establish valuable models additional
resources must be exploited.
Every-day experience shows that a great deal of more or less
quantitative knowledge about the business processes is
available that, so far, could not be represented in a form of
fundamental mathematical models. Thus, it is straightforward to
look for possibilities to incorporate this knowledge into
alternative kind of numerically evaluable process models and
knowledge-based prediction models.
The basis of knowledge-based prediction models is knowledge
from business process experts. Therefore the proposed
application schema should include fuzzy expert system
technique to form easy the basic rules for formulating of
prediction models for the business process. Also, data from
already running business processes cover a wealth of hidden
information about business process dynamics.
6. In our application to develop a data driven models we use a
special category of artificial neural networks – flexible neural
networks. This approach allows choosing automatically the
structure of neural networks adaptable for complexity of the
problem to be solved. For processes with high-dimension
inputs variables we proposed an original modification of deep
belief networks to construct the predictive models with high
generalization properties. To predict simple time series
processes Caterpillar methodology of singular spectrum analysis
(SSA) models are used.
The proposed hybrid modeling approach allows exploiting
collected data in very efficient way by using various sorts of
artificial neural networks and procedures to build data driven
predictive models.
Experience showed that neither fundamental process models,
nor heuristic descriptions, or various artificial neural networks
alone are sufficient to describe real business processes
accurately enough to make efficient process optimization. In
order to meet this requirement, all available knowledge should
be activated. In particular the information hidden in the
extended measurement data records from the process under
consideration must be exploited. Hence, procedures are needed
to simultaneously capitalize the available mathematical
modeling knowledge: fundamental mathematical models, the
information hidden in process data records as well as the
qualitative knowledge gained by the process operators through
their experience. This can be achieved with the ´Hybrid Process
Modeling´ technique.
Figure 2. Elements of hybrid modelling approach for predictive
modeling tasks
7. Extended knowledge embraces more information about
business process and creates opportunities for the development
of more accurate predictive process models. For the reason that
the real business processes usually are non-stationary, the
parameters of the hybrid models should be adapted on-line,
when new observations from the real processes reach the
databases.
2. Intelligent optimization tools
The developed prediction models are the basis for business
processes’ optimization. For the reason that the predictive
models, objective functions and optimization constraints usually
are very complicated in real business processes it is difficult to
apply traditional optimization and search methods for process
optimization tasks. For such cases the proposed general
application schema should be equipped with new established
optimization methods.
The optimization methods are required to have possibility to
combine evolutionary programming methods, genetic
algorithms, ant colony optimization and swarm intelligence
methods. Many successful applications already known in
practice have demonstrated, that these advanced optimization
methods are very powerful new tools for the investigation of a
wide range of optimization problems in real business processes.
3. Intelligent analysis tools
The research and applications of AI techniques in data analysis
include all areas of data visualization, data pre-processing
(fusion, editing, transformation, filtering, and sampling) and
database mining techniques. The proposed general application
schema advocate to focus on methods that can assist in
selecting and extracting the best ‘‘features’’ or condition
indicators from the whole data pool, that contain as much
information as possible.
These indicators present a reduced version of the original data
and preserve characteristic features of the process. Now, the
created indicators can be used to detect “unexpected behavior”
of the processes and to identify process faults.
Various data mining algorithms for process analysis could be
implemented in a proposed general application schema. The
most useful of them are methods based on Principal
Component Analysis (PCA), nonlinear (kernel) PCA, Self-
Organizing Networks, Deep-belief networks and fuzzy logic.
8. Example of AI application in business
FOBISS CM - Cash Management solution
for the Banking industry
Effective cash management is a crucial success
factor for banks or outsourcing companies.
FOBISS CM software offers for banks a Cash
Management solution to automate and improve
cash supply chain efficiency: cash logistics, ATM
and branch cash management (forecasting,
planning and optimization).
Using FOBISS CM software banks can benefit by
having more prime retail space available in
branches, freeing up staff to focus on customer
facing activities, shorter cash lead times,
maximize ATM availability and minimize running
costs.
FOBISS CM system employs artificial intelligence
techniques to control and optimize entire cash
cycle for different types of cash-points devices.
FOBISS CM system allows performance of
intelligent cash-point monitoring, resource
forecasting, cash delivery
management/optimization and business
processes’ analysis.
Traditional mathematical models have some
limitations when applied in cash management
systems (non-linearity, non-stationarity,
unknown relationships.
Artificial intelligence methods (neural networks,
fuzzy logic, evolutionary computation, swarm
intelligence) give more possibilities to design
and implement advanced cash management
systems.
Application areas of FOBISS Cash
Management
● ATM networks
● Automated teller safes
● Cash deposit systems
● Cash recycling systems
● Bank branches networks
9. Most important blocks of FOBISS cash management software are:
Intelligent predictive models of the business processes: employed techniques - support vector
machines, artificial neural networks, fuzzy logic.
Intelligent optimization tools: employed technique – advanced search algorithms, simulated annealing
methods, genetic and evolutionary optimization techniques.
Intelligent communication tools: employed techniques – multi agent systems.
Intelligent analysis tools (on-line detection of “unexpected” process behavior, and detection of process
faults): employed techniques – PCA, kernel PCA, advanced clustering algorithms, Self-Organizing
Networks and Deep belief networks.
Figure 3. Overall structure of the FOBISS CM system
Figure 4. Overall structure of the FOBISS CM system
10. Figure 4. Functionalities of the FOBISS CM system vs. other CM systems
MONITORING
FOBISS CM system uses special multi-agent based technologies to
collect the necessary transaction data and to present the
transaction results in a user friendly graphical environment (cash
demand/supply plots, statistics);
Some special monitoring functions are realized in the FOBISS CM
system:
Ranking of all ATMs in network based on cash demand/supply
volume;
Real-time cash state in every ATM and alerts connected with
high, normal and low level of cash;
Statistics of cash demand/supply for every ATM (day, week,
month);
Real-time cash demand/supply plots together with statistical
averages;
Correlation plots (cash demand/supply) of neighbors’ ATMs.
11. FORECASTING
Exploratory data analysis techniques are used to choose the most
important input variables for forecasting algorithms. Historical data
records, seasonality, holidays, local events are analyzed by forming
input variable groups for cash demand/supply forecasting.
Realized Forecasting algorithms:
● Flexible adaptive artificial neural networks (ANN);
- For every ATM or branch unit an ANN is employed to learn
complex relationships between inputs variables and cash
demand/supply;
- Every new data record is used to retrain the network;
● Adaptive fuzzy expert systems (expert clone);
- Experts’ knowledge is involved to form the initial cash
demand/supply forecasting rules using user friendly fuzzy
logic interface;
- Initial system is then adapted by using historical data and
real-time observation
● Adaptive support vector regression;
- Support vector regression is a special modification of support
vector machines techniques dedicated for solving of
nonlinear regression problems;
- SVR technique is well suited for starting phase of Cash
Management system, when the amount of data records for
ANN training is limited;
- Given training data, the support vector regression solves a
regression curve construction problem, where input variables
are mapped to a higher dimensional linear space by the
special kernel function Φ
12. OPTIMIZATION
FOBISS CM system reduces the cost of cash holdings and
replenishment by estimating optimal cash loads and loading
intervals for each cash point.
Factors which can be taken into consideration by process
optimization:
● Market interest rates;
● Cash supply and Logistics costs;
● Insurance costs;
● Physical constraints (min, max cash amount);
● User defined constraints;
Optimization techniques implemented in FOBISS CM system:
● Empirical combinatorics,
● Simulated annealing algorithms,
● Evolutionary programming methods
Special case: optimal routing for cash upload
Implemented technique: swarm intelligence – ant colony
optimization. Modeling of pheromone depositing by ants in their
search for the shortest paths to food sources is employed for the
creation of advanced shortest path optimization algorithm.
13. INTELLIGENT ANALYSIS
Intelligent analysis and early detection of the unexpected
behavior of the ATMs (or other cash-point devices) is
important for efficient functioning of networks;
Because of high service costs, it is very expensive to employ
human operators to supervise all ATMs (>1000) in an ATM
network;
FOBISS CM system includes an automatic identification
procedure based on auto associative artificial neural
networks (AANN) to supervise continuously the ATM
networks;
Auto associative Neural networks are trained using
advanced deep-auto-encoding procedure (Restricted
Bolzman machine, RBM);
Training procedures induce the neural network to model
correlations in the input data to reproduce the input data at
the output with minimal distortion.
Compression of information by the bottleneck results in the
acquisition of the correlation model of the input data,
useful for performing of further data analysis.
Trained auto associative neural network maps the input
variables into the space of the nonlinear correlation model
and the squared prediction error (SPE) of this mapping can
be used to detect unexpected behavior in the inputs.
14. Bank and ATM network savings
using FOBISS CM
Case 1
Due to improved analytic, decision
making and planning capabilities, a
bank with 6000 ATM network, saves
11,5 Million Euro yearly.
Case 2
After FOBISS CM deployment the bank
(185 branches) managed to decrease
dormant cash level by 41%.
Case 3
Once FOBISS CM was deployed, the
bank (1100 ATMs) started planning
cash delivery more accurately.
Consequently, planned and emergency
visits decreased by 16% and 64%
respectively.
15. CONCLUSION
The potential of Artificial Intelligence for
organizations is enormous. The AI market is
continuing to grow and the way businesses
operate will very soon take up a whole new
meaning, resulting in fewer employees
required while significantly improving bottom
line results.
AI technologies allow companies to automate
and improve complex descriptive, predictive
and prescriptive analytical tasks. AI helps to
understand associations between different
information flowing through companies and
can suggest relevant information to the right
person at the right moment for timely
decision-making. AI applications are intended
to reduce costs, improve customer satisfaction
and productivity and increase revenues.
FOBISS employs various artificial intelligence
techniques for business process and resource
planning, optimization and control. The
efficiency of the proposed technologies was
proved in various pilot projects. The
implemented techniques are modular and can
be suited according to special demands of
users. Systems functionality is easily
configurable and new methods and techniques
are straightforward to be incorporated in the
existing system configuration.
