International Journal of Information Management 33 (2013) 263– 270
Contents lists available at SciVerse ScienceDirect
International Journal of Information Management
j our nal ho me p age: www.elsev ier .com/ locate / i j in fomgt
ata management in cognitive financial systems
idia Ogiela ∗
GH University of Science and Technology, Al. Mickiewicza 30, PL-30-059 Krakow, Poland
r t i c l e i n f o
rticle history:vailable online 27 December 2012
eywords:ognitive systemsata analysisFAIS systems (Cognitive Financial Analysis
nformation Systems)ognitive informaticsognitive systemsBMLRSS systems (Understanding Basedanagement Liquidity Ratios Support
ystems)BMFLRSS systems (Understanding Based
a b s t r a c t
This publication discusses ways of using cognitive analyses for semantically interpreting economic fig-ures. A semantic analysis making use of mathematical linguistic algorithms in order to extract meaningfrom sets of analysed data is illustrated with an example of a class of cognitive systems designed to ana-lyse economic figures, or more precisely, financial ratios. The cognitive analysis systems presented in thispublication are discussed as exemplified by the class of Cognitive Financial Analysis Information Systems(CFAIS). This publication proposes algorithms executed by a broad class of automatic data interpretationand understanding systems designed for the in-depth semantic analysis and interpretation of the resultsobtained. This will be done by defining new system classes as applications supporting decision-makingprocesses, useful in various areas of knowledge.
anagement Financial Leverade Ratiosupport Systems)BMARSS systems (Understanding Basedanagement Activity Ratios Support Systems)BMRRSS systems (Understanding Basedanagement Rentability Ratios Support
ystems)
. Introduction
Computer applications for the automatic acquisition, interpre-ing and description of data can be employed for tasks oriented athe semantic perception analysis of data. The purpose of research’sevelop concepts and create solutions for new classes of systemsupporting the interpretation, description and analysis of datancluding its semantic classification, which can be used to helpn tasks consisting in the complete interpretation and semanticeasoning about selected types of data and information to sup-ort strategic information management. Such solutions are basen the semantic analysis of interpreted data collected in the formf databases as well as isolated (single) data type. The purposef the semantic analysis presented will be to indicate the factorsnd features of the analysed information which can be used tonambiguously conduct the analysis, interpretation and reasoning
rocesses based on features characteristic for selected informationlasses. This is due to the importance of excluding and eliminatingactors and features which turn out to be immaterial during the
analysis conducted, because of the type of the data examined orbecause of the characterisation of features of the analysed patternin the form of a data vector representing economic and financialindicators of key importance for enterprise management.
Cognitive analysis processes are characteristic of the intellec-tual processes running in the human brain, particularly those ofanalysing, interpreting, reasoning, forecasting about specific situ-ations, meanings and the significance of information. The coursesof such processes form the basis for developing intelligent infor-mation systems (Bernstein & Wild, 1999; Buchanan & McMenemy,2012; Chomsky, 1988; Cohen & Lefebvre, 2005; Duda, Hart, & Stork,2001; Laudon & Laudon, 2002; Meystel & Albus, 2002; TalebiFard& Leung, 2011; Zhong, Ras, Tsumoto, & Suzuki, 2003) and cognitivesystems, which play a huge role in data analysis and interpreta-tion studied by cognitive informatics (Ogiela, 2007a, 2007b, 2008a,2008b, 2008c, 2008d, 2008e, 2009, 2010a, 2010b; Ogiela & Ogiela,2009, 2010, 2011a, 2011b, 2011c, 2012; Ogiela & Srebrny, 2012).
Cognitive reasoning consists in analysing and understanding thecontents and the semantics of the data examined. The semantic
analysis is based on a lexical analysis in which the structure ofa given word is used to describe it and to find the meaning ofthe content of the word. The basis for this morphological analy-sis is a specific language to which the above element belongs, and
264 L. Ogiela / International Journal of Information Management 33 (2013) 263– 270
e und
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elappftscggtdgmaaim
mSTpoTsa
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Fig. 1. The cognitiv
he process of understanding results from comparing the previ-usly recorded expectations concerning specific features of dataith the features from the input text. The set of expectations
oncerning the anticipated features of the analysed elements is pro-uced by analysing the knowledge of experts whose expectationsf the meanings of particular situations are presented in the sys-em database. This set of semantic hypotheses is compared with thetream of input data, and this leads to cognitive resonance (Ogiela
Ogiela, 2009, 2012). This process produces a list of selected inter-retations which were found to correspond to the analysed inputata – this leads to the stage of understanding the analysed dataFig. 1).
In the process of data analysis and understanding, it is also nec-ssary to determine how big a role is played by the appropriateinguistic form used to record the sequence of analysed input datand the set of semantic hypotheses formulated by experts. The sim-lest elements of a language are individual letters, i.e. the definedarameters and ratios whose sets can constitute input sequencesor the syntactic analyser. The set of all letters is referred to ashe alphabet, which can be used to construct further words andentences subjected to the analysis. All methods of creating moreomplex expressions from simpler elements are determined by therammar appropriate for the given language. It is the rules of thisrammar that allow recording both the expert’s knowledge andhe methods of transforming elementary parameters into complexescriptions of situations and processes taking place within theiven company. Although these structures, which are sets of ter-inal symbols, are not understandable to a human, this notation
llows the parser to quickly, automatically and effectively analysend understand them. Then, the results obtained can be presentedn a way that is legible to the user, also thanks to the formal gram-
ar applied.Semantic analysis systems dedicated to supporting enterprise
anagement – CFAIS (Cognitive Financial Analysis Informationystems) – focus on the correct choice of the right economic ratios.hese ratios precisely reflect the business activity of a given enter-rise, its standing, its financial result, and are also an expressionf the record and the generalisation of specific economic events.he criteria for classifying financial ratios vary, but the followingplit into groups is the most typical (most frequently found in rationalysis) and the most informative one (Bernstein & Wild, 1999):
liquidity ratios – characterise the resources and the solvency ofthe working assets which form the basis of the current operationsof the enterprise;
erstanding schema.
• financial support ratios (called financial debt ratios) – clarify thesources financing the company assets, and in particular the shareof external capital (short and long-term liabilities) and the relatedeffectiveness of expenditure, i.e. the interest paid;
• turnover ratios – show how quickly assets turn (cycle) and howproductive they are;
• profitability ratios – present the financial effectiveness of thebusiness operations of a given unit by linking the financial resultto the income from sales (of goods and services) and the cost ofthese sales.
The analysis of the economic and financial figures of a companywith the use of CFAIS systems makes it possible to correctly inter-pret these figures and thus enable the management to make thestrategic decisions that are right for the enterprise.
Semantic analysis systems interpret data/information byextracting the semantic content (semantic layers) from these datasets. An important characteristic of this type of analysis is the abil-ity to indicate the directions in which the researched phenomenonmay develop, the changes that may occur in the future, and to deter-mine the degree to which the analysed situation will improve orworsen.
2. Economical cognitive systems
Systems designed for economic and financial analyses are clas-sified to one of three proposed classes of cognitive data analysissystems (Fig. 2):
• CFAIS systems (Cognitive Financial Analysis Information Sys-tems),
• CIAIS systems (Cognitive Image Analysis Information Systems),• CPIAIS systems (Cognitive Personal Identification and Authenti-
cation Information Systems).
The CFAIS class of systems is divided into four sub-classes whichinclude the following new classes of systems cognitively analysingeconomic and financial data:
• UBMLRSS (Understanding Based Management Liquidity RatiosSupport Systems) – cognitive systems for analysing enterprise
liquidity ratios which will reason about the resources and thesolvency of the working capital of the company as well as aboutits current operations based on a semantic analysis of a set ofratios;
L. Ogiela / International Journal of Information Management 33 (2013) 263– 270 265
omica
•
•
•
tacm
dfitia(fceIs
bcss
Fig. 2. The classifiocation of econ
UBMFLRSS (Understanding Based Management Financial Lever-age Ratios Support Systems) – cognitive systems for analysingfinancial leverage ratios (financial debt ratios) which will reasonabout the sources financing the company’s assets and the propor-tion of external capital by analysing short-term and long-termliabilities, as well as the effectiveness of outlays and the interestpaid;UBMARSS (Understanding Based Management Activity RatiosSupport Systems) – cognitive systems for analysing turnoverratios which will reason about how fast assets rotate and howproductive they are;UBMPRSS (Understanding Based Management Profitability RatiosSupport Systems) – cognitive systems for analysing profitabilityratios which will reason about the financial efficiency of busi-ness operations of a given unit based on the relationship betweenfinancial results and the sales of goods and services as well as thecost of sales.
As a result of operating these UBMSS systems, it’s possible toake the right strategic decision for the enterprise and to reasonbout the future on the basis of an analysis of economic ratiosharacteristic for the enterprise, and in particular the financial oracroeconomic indicators portraying its current standing.Automatic data interpretation and understanding systems
esigned for the purposes of the semantic analysis of economic andnancial data are developed by applying the principles of linguis-ic reasoning and semantic interpretation. Such formalisms maket possible to describe and perform a semantic analysis orientedt determining the significant features of the examined figuresratios), and also to anticipate and project the directions of theiruture changes. The construction and structure of the presentedlass of IT systems with the identification of their subclasses is ofxtreme importance due to the increasingly robust development ofT systems serving not just technical science, but all other fields ofcience and knowledge.
Establishing a classification of systems conducting analyses
ased on a linguistic/semantic data analysis is a task that can beompleted by using and building the appropriate classes of sub-ystems for analysing special types of data, which include thetrategic data of an enterprise.
l cognitive information systems.
Cognitive data analysis systems are built by adapting mathe-matical linguistic methods to the tasks of semantic data analysis.Grammatical formalisms were used to build CFAIS cognitive sys-tems. Strategic data of enterprises was analysed using specificeconomic/financial indicators, and during the interpretation pro-cess, it was undergo an analysis and a determination of itssignificance for the further growth of the enterprise (based ondetermining the semantics of the data interpreted). The economicand financial ratios were analysed in four independent groups, i.e.liquidity ratios, financial debt ratios, turnover ratios and profitabil-ity ratios. This approach to the semantic analysis of grouped ratioswill support a cognitive data analysis which is to determine the sig-nificance of the analysed data in the current situation and indicatethe changes that may occur within the enterprise in the future.
3. Examples of cognitive CFAIS systems
Cognitive systems analysing financial ratios of enterprises arepresented for various classes of economic and financial ratios.These systems belong to the UBMLRSS (Understanding Based Man-agement Liquidity Ratios Support Systems) class, i.e. cognitivesystems analysing liquidity ratios of a company to reason aboutits resources, the liquidity of its working assets and its currentoperations.
The first example of cognitive ratio analysis systems which ispresented comprises systems determining the liquidity of compa-nies. The liquidity of an enterprise is assessed using liquidity ratioswhich make it possible to assess the solvency of a given businessunit and its ability to settle its current payables on time. This canform the basis for evaluating the financial standing of an enterpriseby linking assets (sometimes including prepayments) to short-termliabilities (maturing in less than a year). In addition, the analysiscan be extended to current liabilities, e.g. special funds, accruedexpenses and deferred income. The following types of liquidityratios are then distinguished (Bernstein & Wild, 1999):
(a) current ratio;(b) quick ratio;(c) cash to current liabilities ratio;(d) balance of payments.
266 L. Ogiela / International Journal of Informat
orl
s(
G
wVs
Fig. 3. VTl − the set of terminal symbols.
The analysis executed by CFAIS systems makes use of two typesf liquidity ratios – the current ratio and the quick ratio – whichepresent the most fundamental methods of assessing enterpriseiquidity.
A formal grammar of the following form was defined for theemantic analysis, by CFAIS systems, of the liquidity of a companyOgiela & Srebrny, 2012):
l = (VNl, VTl, SPl, ST)
here VNl – the set of non-terminal symbols; VNl – {SOLVENCY,ERY GOOD, GOOD, POOR, VERY POOR}; VTl – the set of terminalymbols; VTl – {a, b, c, d, e} – Fig. 3.
where a ∈ [0; 1), b ∈ [1; 1.2), c ∈ [1.2; 1.5), d ∈ [1.5; 2), e ∈ [2; +∞)ST ∈ VNl, ST = SOLVENCYSPl – set of productions:
1. SOLVENCY → VERY GOOD | GOOD | POOR | VERY POOR2. VERY GOOD → DB | DC | DD // liquidity = very good (optimal)
3. GOOD → CB | CC | CD | DA (*) | DE | EB (*) | EC | ED (***) //
liquidity = good4. POOR → BB | BC | BD (**) | BE (**) | CA (*) | CE (**) | EA (*) | EE //
liquidity = poor
Fig. 4. An examples of CFAIS system
ion Management 33 (2013) 263– 270
5. VERY POOR → AA | AB | AC | AD (**) | AE (**) | BA // liquid-ity = very poor
6. A → a7. B → b8. C → c9. D → d
10. E → e
Symbols of semantic actions for some situations (providingadditional information):
(*) caution = a lot of funds frozen in the inventory;(**) caution = no inventory and/or the decisive majority of funds
in accounts receivable and on bank accounts;(***) caution = excess liquidity.The semantic analysis of the value of liquidity ratios produced
the following example results (Fig. 4).Fig. 4 shows an example CFAIS system which, for the current
ratio of 1.73 and the quick ratio of 1.02, assesses the solvency of thecompany as very good and points to its very good management.Also Fig. 4 presents an example CFAIS system which, for the cur-rent ratio of 1.92 and the quick ratio of 0.13, assesses the solvencyof the company as good and indicates that too much of the com-pany’s funds is frozen in its inventory. Another example of CFIAS
system which, for the current ratio of 1.01 and the quick ratio of3.75, assesses the solvency of the company as poor and indicatesthat the company’s liquidity and its inventory are too low. Com-pany D is an example of CFAIS system which, for the current ratio
of the liquidity of a company.
L. Ogiela / International Journal of Information Management 33 (2013) 263– 270 267
et of t
opt
jettmispstg
iaiissflci
flfi
s(
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wFt
12
3
4
56
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fl
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Fig. 5. VTc-f − the s
f 0.48 and the quick ratio of 0.72, assesses the solvency of the com-any as very poor and indicates a precarious financial standing ofhe company.
The CFAIS system for assessing enterprise liquidity supports notust analysing selected financial ratios, but as it is enhanced withlements of semantic data analysis, it primarily indicates the direc-ions of activity and development for the given enterprise. In Fig. 4,hese directions were presented in the form of messages to the
anagement, and if these were followed, it would be possible tomprove the financial liquidity of the company. The semantic analy-is, as an element of an in-depth analysis of data supplemented withrojection elements indicating the (more or less distant) futuretanding of the enterprise shows in which direction the rehabilita-ion or the improvement of the current company situation shouldo.
Another class of systems cognitively analysing economic datas one used to assess the structure of company cash flows. Forn organisation to arrange the appropriate cash flows, it has to,nter alia, determine at what time and for how long increasednvestment expenditure is to be made, whether assets should beold, plan borrowing etc. For this purpose, after analysing severalcenarios of cash generation and expenditure, a preliminary cash-ow statement is drawn up. This is used to analyse the economiconsequences of the calculated balances representing operating,nvestment and financial activities in each analysed scenario.
The CFAIS system conducting a semantic analysis of the cash-ow structure uses the balances of the operating, investment andnancial activities from the preliminary cash-flow statement.
A formal grammar of the following form was defined for theemantic analysis, in CFAIS systems, of the cash-flow of a companyOgiela & Srebrny, 2012):
c−f = (VNc−f , VTc−f , SPc−f , ST)
here VNc−f – the set of non-terminal symbols; VNc−f – {CASH-LOW, FAVOURABLE, UNFAVOURABLE, RISKY}; VTc−f – the set oferminal symbols; VTc−f – {a, b} – Fig. 5.
Where a ∈ [0; +∞), b ∈ (−∞; 0)ST ∈ VNc−f, ST = CASH-FLOWSPc−f – set of productions:
. CASH-FLOW → FAVOURABLE | UNFAVOURABLE | RISKY
. FAVOURABLE → AAA | ABA | BBA // cash-flow = favourable, mes-sage = good liquidity, company growth and creditworthiness,good investment effectiveness
. UNFOURABLE → BAB // cash-flow = unfavourable, mes-sage = permanent financial difficulties, poor chances ofimproving the standing
(***) message = there is no threat of a permanent loss of liquidity,ut temporary financial difficulties may occur.
erminal symbols.
(****) message = spending funds previously accumulated, in thelong run poses a risk of bankruptcy.
The semantic analysis of the value of cash flow balances pro-duced the following example results (Fig. 6).
Fig. 6 shows the situation of a company whose cash-flow struc-ture has been assessed as very favourable due to the balance fromoperating activities amounting to 800, the balance from investmentactivity to −200 and the balance from financial activity to 500.In this case, the CFAIS system assesses the standing of this com-pany as a rising company. Also shows the situation of a companywhose cash-flow structure has been assessed as risky due to the bal-ance from operating activity amounting to −800, the balance frominvestment activity to 400 and the balance from financial activityto 100. In this case the CFAIS system indicates that a financial riskto this company may appear.
Fig. 6 shows the company G whose cash-flow structure hasbeen assessed as risky due to the balance from operating activityamounting to −800, the balance from investment activity to −300and the balance from financial activity to −500. In this case, theCFAIS system indicates a risk that the company will go bankruptin the long term. Company H is an example of cash-flow structurehas been assessed as very unfavourable due to the balance fromoperating activity amounting to −100, the balance from investmentactivity of 700 and the balance from financial activity of −300. Inthis case, the CFAIS system indicates permanent financial difficul-ties of the company.
The CFAIS system for assessing enterprise cash-flow supportsnot just analysing selected financial ratios, but as it is enhancedwith elements of semantic data analysis, it primarily indicates thedirections of activity and development for the given enterprise. InFig. 6, these directions are presented in the form of messages to themanagement.
In order to improve the standing of companies, the semanticanalysis conducted can cover their various economic and financialratios, including the following: enterprise financing with externalcapital as well as improving the return on equity.
Yet another class of systems cognitively analysing the financialstanding of an enterprise analyses that enterprise’s debt to deter-mine the level of that debt and the enterprise’s ability to service it.In order to carry out this type of analyses, the following types ofratios can be considered:
• total debt ratio,• long-term debt ratio,• liability structure ratio,• debt to equity ratio,• long-term debt to equity ratio and• the ratio of long-term liability coverage with net fixed assets.
In the discussed class of systems cognitively analysing com-pany debt, the total debt ratio and the long-term debt ratio wereadopted for assessing the indebtedness of the company. Based onthe debt ratios selected for analysing, a sequential grammar havingthe following form was formally defined:
GD = (VND, VTD, SPD, ST)
where VND – the set of non-terminal symbols; VND – {DEBT, HIGH,LOW, NONE}; VTD – the set of terminal symbols; VTD – {a, b} – Fig. 7.
Where a ∈ [0; 0.57), b ∈ [0.57; 0.67], c ∈ (0.67; +∞)ST ∈ VND, ST = DEBT
268 L. Ogiela / International Journal of Information Management 33 (2013) 263– 270
stem
12
3
4
567
r
Fig. 6. An examples of CFAIS sy
SPD – set of productions:
. DEBT → OPTIMAL | HIGH | LOW.
. OPTIMAL → BB | BC // debt = optimal, message = the best shareof external capital in financing the company’s assets, with anoverwhelming proportion of long-term liabilities in the totalcorporate debt.
. HIGH → CB | CC // debt = high, message = high financial risk to thecompany, the need to repay loans including interest in case thefinancial standing of the company deteriorates in the future, anda low proportion of long-term liabilities in the total corporatedebt.
. LOW → AA | AB | BA // debt = low, message = the level of companyindebtedness falls, the financial independence of the companyrises and there is a low share of long-term liabilities in the totalcorporate debt.
. A → a.
. B → b.
. C → c.
Results of the automatic reasoning about and analyses of debtatios are shown in Fig. 8, which presents an example of a company
Fig. 7. VTD− the set of terminal symbols.
of the cash-flow of a company.
characterised by the optimum level of the total debt ratio 0.62 and asimilar value of the long-term debt ratio 0.59. This situation repre-sents the best share of external capital in financing the company’sassets, with an overwhelming proportion of long-term liabilities inthe total corporate debt.
The second example portrays the situation of a company with ahigh total debt ratio 0.83 and a low ratio of long-term debts 0.56.This situation represents high financial risk to the company, theneed to repay loans including interest in case the financial standingof the company deteriorates in the future, and a low proportion oflong-term liabilities in the total corporate debt.
The third example portrays the situation of a company with alow financial debt ratio 0.21 and a low long-term debt ratio 0.17.This means that the level of company indebtedness falls, the finan-cial independence of the company rises and there is a low share oflong-term liabilities in the total corporate debt.
The above examples of debt ratio analyses show how finan-cial ratios determine the future, i.e. the growth or the collapse ofcompanies. A semantic analysis of financial ratio levels (the ana-lysed parameters) allows the decision-making group to take theright decisions on ways of repaying debts and the degree to whichindebtedness should be increased.
In processes of extensive (in-depth) analyses of financial ratios,
the semantics of the analysed data allows the right strategic deci-sions to be taken with regard to corporate operations based on amore in-depth analysis of the meaning of a given group of ratios toensure the correct operation and growth of the enterprise.
L. Ogiela / International Journal of Information Management 33 (2013) 263– 270 269
syste
4
patSosstimcbttap
oaen
dbectctSaep
Fig. 8. An examples of CFAIS
. Conclusions
The essence of the presented solution is the semantic inter-retation of strategic data of enterprises achieved by conducting
cognitive analysis of the interpreted data which illustrate cer-ain economic phenomena occurring within these enterprises.uch situations are usually a component of the fluctuationsf the selected financial indicators. Building systems for theemantic analysis of data strategic for enterprises will, to aignificant extent, help to manage these organisations bet-er, more efficiently and more rationally. The research andts results are also contribute to a more efficient manage-
ent of companies and assessments of the impact of financialhanges occurring within an enterprise and their significanceoth in the present and the future. It will also be possibleo use the proposed solutions to evaluate the external situa-ion (of the environment) of the enterprise by a forecastingnalysis which forms a component of cognitive data analysisrocesses.
Thus the solutions discussed here contribute to the devel-pment of scientific subjects in the area of the theoreticalspects of cognitive data analysis and to the development of thentrepreneurial sector within the scope of the proposed solutions,amely the CFAIS systems and their four sub-classes.
Cognitive CFAIS systems for the semantic analysis of financialata can analyse various financial ratios. It is possible to com-ine a greater number of ratios for a more in-depth and detailedxamination (assessment) of the given company’s standing andharting directions for the future. A semantic analysis suppor-ing decision-making processes makes it possible to describe theompany standing and at the same time to chart the direc-ions for improving the unfavourable standing of the company.
upplementing this data analysis with elements of a semanticnalysis makes it possible to target decision-making processes atxtracting semantic information to be used in cognitive analysisrocesses.
m of the debt of a company.
Acknowledgment
This work has been supported by the National Science Centre,Republic of Poland, under project number 2012/05/B/HS4/03625.
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