Company Presentation

Company Presentation

Agenda 

1 

About Us 

2 Snapshot – Areas of Activities 

3 

Methodology & Tools 

4 

Case Studies 

5 

Contact 

05.06.2015 KIANA Systems – Company Presentation 2

About us 

• Spin-Off of the German Research Center for Artificial Intelligence (DFKI) 

• Founded in 2001 as a spin-off of largest AI research institution worldwide to 

take latest research in the areas of AI and machine learning to industry 

• Industrial applications for finance & insurance, retail, pharma & medtech and 

manufacturing 

• Main areas of activities 

• Data Mining 

• Development of machine learning algorithms / self-learning software 

• Business Intelligence 

• Research in emergent data mining and data mining & compliance 

• Team 

• Senior data scientists with decades of experience from industry and research 

• Computer Science / Engineering / Maths degrees; many PhD-level 

• Cooperations with world-class research institutions 

• Established network of domain and subject-matter experts 


What we do 

• Data Mining 

• Data analysis for defined tasks 

• Deliverable: reports with suggestions for actions / analytics dashboard 

• Example: churn modelling for insurance 

• Development of machine learning algorithms / self-learning software 

• Data-driven approach 

• If algorithmic solutions are missing or when dealing with highly dynamic data 

sets 

• Deliverable: self-learning software, initial models for problem solving 

• Maintenance of models as a service 

• Example: software for classifying tablets for automated packaging process 

• Business Intelligence 

• Software to build analytics infrastructure 

• Data collection and cleansing for BI systems 

• Example: BI infrastructure for German organic supermarket industry 

• Research 

• Together with the Fraunhofer-Institute (ITWM) developing emergent data mining 


Management Team 

Dr. Mathias Bauer - President / Chief Scientific Officer 

• 14 years of industrial experience in data mining and adaptive software 

• 15 years of research in AI & machine learning at German Research Center 

for AI 

• over 70 int. publications; editor of Journal of User Modeling and User- 

Adapted Interaction 

• Studies in Mathematics and Computer Science; MSc and PhD (Uni 

Saarbrücken) 

Patrick Brandmeier - CTO 

• 14 years of industrial experience in data mining and adaptive software 

• 8 years of research in AI & machine learning at German Research Center for AI 

• Studies in Computer Science; MSc Computer Science (Uni Saarbrücken) 

Ushananthan Ganeshananthan - Head of Business Development 

• 10 years of experience in corporate development, investment banking and 

building data-driven companies 

• Prior to KIANA, Vice President Corporate Development at Bertelsmann 

• Studies in Engineering, Computer Science and Management at KIT, Oxford, 

Columbia and Berkeley; MEng (Oxford) and MBA (Columbia) 


Research cooperations 

Fraunhofer Institute for 

Industrial Mathematics 

Management Information Systems / 

Natural Language Systems 

German Research Center for Artificial 

Intelligence 

Information Systems and 

Machine Learning Lab 


Some of our clients 


Agenda 

1 

About Us 


3 


4 

Case Studies 

5 

Contact 


Snapshot – Areas of Activities (exemplary) 

Industry / 

Functional 

Area 

Strategic 

Planning / R&D 

Marketing 

Finance & 

Insurance 

• Fin. health 

monitoring 

(Case 1) 

• Churn 

modelling 

(Case 2) 

Retail 

• BI 

infrastructure 

(Case 5) 

• Cross-channel 

synergies 

Pharma & 

Healthcare 

• Lab analytics 

(Case 6) 

Manufacturing 

• Internet of 

Things 

Utilities & 

Natural 

Resources 

• Smart Grid 

• Churn 

modelling 

Transportation 

& Logistics 

• Network 

planning 

• Customer 

segmentation 

Sales 

• Next-best 

offer 

• Stock out 

prevention 

• Salesforce 

effectiveness 

• Adapt. Web 

(Case 8) 

• Offer 

optimization 

Distribution 

• Branch 

analysis 

• Cross-channel 

synergies 

• Supply chain 

optimization 

• Web analytics 

Operations & 

Production 

• Automated 

valuations 

(Case 3) 

• Personnel 

planning 

• Advanced 

automation 

(Case 7) 

• Predictive 

maintenance 

(Case 9) 

• Failure 

prediction 

• Predictive 

maintenance 

Aftersales 

• Call center 

analytics 

• Supply chain 

performance 

• Smart billing 

• Call center 

analytics 

• Return 

shipments 

reduction 

Administration 

• Fraud 

detection 

(Case 4) 

• Working 

capital mgmt. 

• Document 

management 

• Collections 

optimization 


Agenda 

1 

About Us 


3 


4 

Case Studies 

5 

Contact 


KIANA approaches projects using a well-tried 

methodology 

Business Case 

Analysis 

Data 

Collection 

Data 

Analysis 

Solution 

Design 

Solution 

Implementation 

Experienced 

KIANA 

consultants meet 

clients, analyze 

their business 

models, 

understand the 

strategic issues 

and identify 

potential value of 

existing data and 

its usage within 

the clients’ 

businesses 

KIANA IT experts 

support company 

to collect, 

cleanse and 

aggregate the 

necessary data 

sets to run 

analyses 

KIANA data 

experts 

(experienced and 

qualified 

mathematicians 

& statisticians) 

use data to 

extract insights 

with help of 

quantitative 

models and 

algorithms 

KIANA data 

experts and 

consultants 

identify powerful 

solutions and 

operational 

improvements 

increasing 

performance for 

the clients’ 

strategic issues 

based on data 

models 

KIANA 

consultants 

support the client 

with 

implementing the 

identified 

solutions. 

KIANA also 

develops 

software to 

implement 

solution for 

client. 


KIANA saves time and money by using an iterative 

solution design 

Old approach: 

Our approach: 

High investment in 

tools & software 

Data 

Analysis 


Learning 

Tests and learning 

Investment 



KIANA works with a wide range of proprietary and 

third-party tools 

Programming languages: 

• Java, C#, C++, Prolog, Lisp, VisualBasic, Python, Eclipse, RCP, RAP, Splus 

DB / BI: 

• Oracle, Microsoft Access, Microsoft SSRS Reporting Services, Microsoft SQL Server, 

Postgres, MySQL, Qlikview, SAP 

Big Data: 

• Hadoop 

Machine Learning: 

• Knime, R, S 

Information Retrieval: 

• Gate 

Exemplary: proprietary tools for finance: 

• KVal: valuation tool for structured objects 

• KBil: tool to detect anomalies in annual accounts 

• KGPS: tool to assess financial health of companies 

• KSegmenter: tool to segment debtors into different groups with respect to target 

variables 


Agenda 

1 

About Us 


3 


4 

Case Studies 

5 

Contact 


KIANA has developed a web-based tool for assessing 

a company’s financial health 

• Background: KIANA developed a quick-assessment tool which 

evaluates the financial health of small and mid-sized companies 

• Task: Development of a financial assessment tool based on financial KPIs 

which allow to detect liquidity problems 

• Solution design: Identifying scores and corresponding weights which reflect 

a company’s financial health and which allow a quick assessment 

• Result: The tool has been used to analyse various companies and classified 

them with a high accuracy 

Case 1 


The tool combines KPIs from different areas to assess 

financial health 

Solvency Earnings Situation Qualitative factors 

• Liquidity 

• AR 

• Liabilities 

• Legalisation 

Score 1 

Weight 3 

• Cashflow 

• Historic periods 

• If available, 

financial forecasts 

Score 2 

Weight 2 

• Risks for going 

concern 

• Predominantly 

prognostic 

Score 3 

Weight 1 

Assessment of financial status 


The tool summarises the assessment in aggregated 

scores 

Stage Status Aggregate Score 

1 robust financial health 100 – 149 

2 healthy, however can be improved 150 – 249 

3 financial risk recognisable 250 – 349 

4 high financial risk 350 – 449 

5 insolvency highly likely ≥ 450 


KIANA has helped an insurance company to reduce 

churn by about 50% 

Case study: churn modelling for insurance company 

Case 2 

• Background: An insurance company wanted to identify and predict potential 

leavers within customer population, so that it could apply measures for 

retention before customers actually depart 

• Task: Identification of potential leavers in contents and building insurance 

• Solution design: Customers were described by a number of different 

attributes and then microsegmented into homogenous groups with high or 

low propensity to churn with the help of decision tree models 

• Result: Using the model client could discriminate and predict potential 

leavers and loyal customers far better and hence apply more targeted 

measures of retention. This led eventually to a reduction of churn by about 

50%. 


Interesting microsegments have been identified using 

a decision-tree model 

• Results: contents insurance (segment labelling removed) 

K 

29.7 | 99% 

12.4 | 73% 

Segment 1 

Segment 2 

3.0 | 68% 

100 | 52% 

2.8 | 81% 

1.6 | 31% 

1.1 | 78% 

0.1 | 22% 

3.7 | 61% 

1.4 | 45% 

4.2 | 44% 

NK 

37.1 | 5% 

0.8 | 9% 

0.7 | 88% 

0.4 | 82% 

0.4 | 36% 

0.5 | 32% 

Segment 16 

Predominantly leavers 

Predominantly loyal customers 

Very interesting segments 

K = leavers 

NK = loyal customers 

1.1 | 78% 

1.1 % of population 78% belong to class K 


Examples: segment 1 and 16 

• Segment 1 (99% leavers) 

• 100% "XL customers" (more than one 

contract) 

• „old tariff" exactly average 

• „Third-party debtor" average 

• 30% more claims and no settlement-claims 

on 

average in the year of 

cancellation 

• Segment 16 (5% leavers) 

• 100% "XL customers" 

• „old tariff" slightly above average 

• „Third-party debtor" slightly below average 

• Average claims history 

• Main differentiator 

• "Remaining balance" (Number of current 

contracts – Number of cancelled contracts) 


KIANA has developed an algorithm to automatically 

value ships for one of the largest classification societies 

Case study: automatic valuation of ships 

Case 3 

• Background: KIANA was asked to develop an algorithm which values 

structured objects, in this case ships, automatically 

• Task: Identifying similar objects and deriving valuation from the valuation of 

those 

• Solution design: Identifying relevant features of objects and suitable 

distance measure; derivation of valuations from valuations of neighbours and 

cross-validation of model 

• Result: Model is being deployed and used on a day-to-day basis; significant 

reduction in time spent on valuations 

H2 

H0 

H1 

Val(H0)=f(H1, dist1, H2, dist2) 

dist2 

dist1 


Valuation procedure (sketch) 

Ship 

Similarity search 

Peer group 

Member 1 

Member 2 

Member n 

Transformation of the prices 

to the estimation date 

using price bands 

Estimated price 


Process steps 

• Data cleansing 

• Identifying and 

removing artefacts / 

measurement errors 

• Smoothing 

Example from ship valuation 

• Calculation of bands 

• Splitting into different 

bands 


Further valuations and features 

• Historical price curve of a ship 

• Estimation of scrap value 

• Estimation of charter rates or OPEX of a ship 

• LTAV (Long Term Asset Value) 

• Asset Comparison 

• Automatic identification of sister ships by several technical attributes 

• Valuation of released sale prices 


KIANA has developed a self-learning solution for 

detecting anomalies in financial statements 

• Background: The analysis of financial statements used to be highly 

dependent on the respective evaluator and her expert knowledge 

• Task: Development of a self-learning system based on a data set of 8,000 

company accounts analyses 

• Solution design: Model-based classification of company accounts; 

abstraction of human expert knowledge from 8,000 processed financial 

statements 

• Result: Solution is used by various agencies for fighting white-collar crime; 

led to a reduction of the revision effort. In almost all tests to date, 95% of 

manipulations were detected 

Case 4 


Three models were integrated 

• Model 1 

• Breach of variance corridor 

1 

2 

3 

4 

5 

6 

7 

8 

9 

• Model 2 

• Conspicuous combinations of deviations, variance patterns 

• Metamodel 

• makes the final decision based on the individual conclusions of model 1 

and model 2 


Schematic test process 

paper documents 

Scanner 

OCR 

electronic 

document 

preprocessing 

preprocessed, anonymized data 

training 

samples 

training 

component 

models 

classifier 

decision 


KIANA operates a retail panel for organic and health 

food stores and offers BI solutions for this segment 

• Background: KIANA operates the entire IT for bioVista GmbH, 

a market research firm, specialised on the organic and health food stores 

segment 

Case 5 

• Task: Sales data (at the granularity level of single items) are collected in 

several hundred organic and health food stores, and then analysed and sold 

to customers. Approximately 1.1bn transaction records are processed. 

• Solution design: KIANA developed a custom data warehouse and BI solution. 

The dataware house solution is based on an Oracle data base combined with 

PostgreSQL; the analysis components are implemented in Microsoft SSRS 

and a custom Access-Excel-combination. The results of the analyses (market 

share data, store comparisons, price-sale correlations and suchlike) are 

presented appropriately to the recipient's data analysis capabilities – ranging 

from simple diagrams and Excel tables to preconfigured multidimensional 

OLAP cubes. 

• Result: bioVista was able to gain market leadership with the help of KIANA‘s 

BI solutions. bioVista is currently expanding to the French market. 


KIANA developed software to analyse large amounts 

of mass spectrometry lab data 

• Background: The molecular biology lab at ETH Zuerich needed to 

analyse large quantities of mass spectrometry lab data quickly for many 

different cases and wanted to facilitate the analysis of data for as many 

scientists as possible with limited programming knowledge. 

Case 6 

• Task: KIANA was asked to develop analytics tool based on novel methods for 

LCMS data. 

• Solution design: KIANA developed an open source framework for rapid and 

interactive development of LCMS data analysis workflows in Python which 

enables experimenting with new analysis strategies for LCMS data as simple 

as possible 

• Result: ETH Zuerich was able to refute the NASA study with the help of the 

software in rapid time. The software is provided as open source to users and 

developers of LCMS data analytics modules: 

http://www.spiegel.de/wissenschaft/natur/arsen-bakterien-gfaj-1-durch-neue-studien-inzweifel-gezogen-a-843305.html 

http://emzed.ethz.ch/ 


KIANA has helped several pharmaceutical companies 

to optimise production processes in real-time 

• Background: Customer wished to produce customised blister 

boxes with tablets and pills for each patient in its new production facility – 

Patient receives her weekly drug ration in a blister box, where drugs are 

correctly sorted into 4 packs (depending on the intake time) for each day in 

the week 

Case 7 

• Task: The task was to accurately sort the correct drug (out of 1,000 different 

drugs) into the correct pack of the blister box – with a false positive rate < 10 - 

6 

, max. 5 msec processing time for each drug identification and classification 

and simultaneous minimisation of the false negative rate 

• Solution design: By measuring the spectrum of each drug with NIR 

spectroscopy within a very short amount of time, data on the chemical 

composition were gathered, which then served as input for the development 

of the classification algorithm based on Fisher’s discriminant 

• Result: The software satisfied performance expectations and was integrated 

into the inspection module of the blister machine. Meanwhile a similar 

solution for another customer has been developed, which correctly identifies 

and classifies drugs that are sealed in foil 


Classification of drugs 

Monitoring the packing process of customised weekly drug rations 

• Complete monitoring and control of all drugs 

• Real-time requirement (~3 ms) 

• Strict requirement on precision 

• Accurate discrimination between over 1,000 drugs types 

NIR spectroscopy 

Machine learning 

Models for the 

classification of 

each single drug 

type 

Spectrum 

A / D - Converter 

Spectrometer 

Illumination 

Fiber Cable 

Flexible Mirror (X 

and Y direction) 

Camera 

Evaluation Unit 


Classification of drugs 

© 7x4-Pharma 


KIANA has developed an adaptive car recommender 

system for an automotive company 

Case study: adaptive car recommender 

Case 8 

• Background: An automotive company wanted to create an intelligent, 

adaptive car recommender that supports the visitors to configure just the 

right car for them 

• Task: Develop the analytical model to determine appropriateness of certain 

configurations for a particular person 

• Solution design: Data from the New Car Buyers Survey and visitors’ own 

configuration was used to identify customer segments of auto buyers with 

their preferences for different auto types 

• Result: The system uses this model to classify a new visitor based on his/her 

input and answers to car and lifestyle questions in order to make optimal 

recommendations 


Several demographic and product data points were 

used to identify preferences of customer segments 

User Profile 

Demographic data 

Last car bought 2 

Catalogue of all 

available car types 2 

Attitude towards cars 

Newly purchased car 2 

Requirements / Usage 

of car 

Scoring function 

f(car type, customer) 

Hard constraints 1 

Relationship 

User Profile Car type with 

technical features 

1 

budget, type of drive,body type, brand, … 

2 

with technical features 

34 


Results of analysis: several clusters of auto buyers 

with type, price and upselling preferences 

Cluster 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5 

Volume Model 

(A, B, C) 

TT 

Upper segment/ 

Premium model 

(D, E) 

Segment focus 

Realised prices 

(paid total price for car) 

44,5 

18,1 

37,4 

55,5 

Balanced, 

Luxury / 

premium 

for D, F, E 

very high 

47,9 

14,2 

37,9 

balanced 

For all models 

balanced 

66,3 

4,3 

2,2 

15,6 

52,1 33,7 

38,5 65,8 

29,4 

36,3 

50,2 

Middle segments 


lower 

61,5 

Middle segments 


lower 

34,2 

Luxury / 

premium 

For all volume 

Models high, 

For luxury / 

Premium 

average 

Pricing potential 

very high 

average 

low 

low 

high 

Upselling 

(Comparison model 

Previously bought vs. 

Current purchase) 

Upselling potential 

Volume mod. 

static, 

For luxury/ 

premium 

Preference 

Towards F 

average 

High propensity 

for all 

classes, 

High preference 

for F 

very high 

High propensity 

A -->B, 

Luxury/ 

Premium 

static 

low 

Highest tendency 

B --> C, 

Luxury/Pre- 

Mium static 

no tendency 

Towards TT 

low 

High pro- 

Pensity for all 

classes, 

for mostly repurchasers 

high 


KIANA has executed several pilot projects in 

monitoring machines (Predictive Maintenance) 

• Project I: Demonstrator in the SmartFactory Kaiserslautern 

Case 9 

• Project II: Monitoring of traversal cranes at steel producer Dillinger Hütte 


Application cases for Predictive Maintenance 

• Monitoring of state of production facilities 

• Prediction of machine failure or critical state 

• Data sources 

• sensor data, operating data 

• maintenance protocols, error reports 

• Derivation of model of undesirable system states 

• => data mining 

• Methods: statistical methods, survival analysis, Hidden Markov Chains, 

anomaly detection 

• Outlook: every important hardware component has its own data mining 

capability and methods will be developed how to combine individual models 

into one global model (emergent data mining) 


Agenda 

1 

About Us 


3 


4 

Case Studies 

5 

Contact 


Contact 

KIANA Systems – a brand of mineway GmbH 

Ushananthan Ganeshananthan 

Im Helmerswald 2 

66121 Saarbrücken 

Germany 

+49 (0)681 / 500 6440 

+49 (0)172 / 285 7012 

kgushan@kiana-systems.de

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