Unicsoft is a trusted data science consulting and software development partner for technology companies

We have strong and long-lasting relationships with customers who recommend us to other businesses. We get most of the new projects via our customers’ referrals.

We are driven by our customer’s success

Unicsoft enjoys connecting great data science experts with great customers

Unicsoft increases partner’s brand value by complementing their stack with innovative Data Science technologies.

We build business-focused solutions and bring sensible value.

Our Strength

Proven experience
in Data Science consulting and Solution
as a service implementation

Using Top-notch technologies
we deliver things, which others
only thinking about doing

Highly competent and stable team
of data analytics and engineers
with the minimum 5 years of experience

Our value
We bring value to software development agencies by:

Showing the ways of getting a competitive advantage for their clients.

Empowering expertise in the business domains and data analysis fields.

Leading our partners to success by attracting more clients and bigger deals.

We bring value to software product companies by:

Increasing user retention by implementing behavioral & predictive analytics solutions.

Improving monetization by learning how to turn the data into value-added insights.

Increasing their brand value by complementing their stack with innovative Data Science technologies.

We bring value to startups by:

Focusing on goals and ideas, rather than specifications and tech tasks. Being agile and effective with the changing requirements.

Speeding up product implementation without the necessity to recruit additional staff or employ complex business models.

Building long-term stable relations leading to minimizing delivery risk.


Solution as a service

What is this?

This model enables you to delegate the issue solving process to Unicsoft, starting from business analytics, goals definition and ending with the solution’s delivery and support.

What are the advantages?

Easy-to-use. The collaboration with and control over the team is simple: you will highlight the requirements and get weekly progress report

All-in-one. Unicsoft has proven expertise in delivering software products from idea to market, including business analysis, UI/UX design, project management and quality assurance.

Result-oriented. You concentrate your efforts on reaching business goals, while the technical side is covered by the partner with robust experience in Data Science product delivery.

When to use this model?

This model works best for the businesses which need assistance of the self-managed and independent team to analyze available data and identify the value, ways of monetization and the solution’ implem

Team extension

What is this?

This model enables you to choose developers from the Unicsoft established in-house team through your standard hiring process, and manage them directly as your team members.

What are the advantages?

Transparency. You have full control over the development process. Unicsoft developers follow your internal processes and integrate

Scalability. Unicsoft takes over all risks connected with human resource management, including selection of the best candidates, their replacement if needed, and scaling up your team without extra costs.

Team fusion. The team is stable and is involved solely into your product on a long-term basis. Therefore, the team are completely imbued with the idea of the product, which leads to better performance.

When to use this model?

This model works best for businesses which develop and support long-term projects through reducing IT costs. It is most efficient for those who can effectively manage the development team, but need to add velocity or expand technology and industry expertise.


Solution as a service

For technology companies and enterprises, our structured methodology generates business, innovation growth and new possibilities for our clients

Business domain analysis and the goals definition

Analysis of available data and improvement of data quality

Proof of concept solution – MVP

Solution development, testing and deployment

Support: regular monitoring adjustment (re-learning) of solution efficiency

Team extension

Our methodology enables choosing best talents who have already proved their proficiency as well as being flexible and scalable at any moment.

Candidate requirements analysis

Get the CVs of relevant candidates from our team

Evaluate candidates according to your process

Select the successful candidate and sign the agreement

Put the engineer on board

Industry and Technical expertise
Tools and techniques


Programming languages

Case study


  • Service type: Dedicated data science team
  • Business domains: FMCG, commerce/retail
  • Expertise: NLP, Text Mining, Data Science
  • Technology and tools: Python, NLTK, Apache Solr

According to latest researches, lack of a stock organization and management causes around 8% of revenue loss, so weeding out that problem impacts on money flow in egregiously positive way. The largest Central & Eastern Europe retailer asked Unicsoft faced a need of a tool that would be a capstone of category management: having over 50000+ of PLUs and getting a dozens of new names every couple of days, Client had only a manual tool for category definition. So far, the basic intention of a tool was a creation of “tag clouds” supposed to tell one sub-categories from the other ones based primarily on a descriptive information, and, consequently, for proper management of PLU turnover, placement and promotion.

First step was defined as data preparation and enrichment : given more than 50k PLU names alongside with variety of features (main category name, manual tags, supplier notes, etc.), a main thing was to set up an proper DWH solution for storage and handling with further data pre-processing activities. Starting with SQL-type DB combined with Python libraries for pre-processing, a huge bunches of data was cleaned up and enriched. Next step was a tokenization of data: main goal was to create a tags cloud  for working out subcategories : I.e, given the “sweets” category through applying these methods based on keywords, a list of sub-categories came up (diabetic sweets, soya sweets, etc.). Subsequently, that approach was applied to entire portfolio of PLUs providing opportunity not only to categorize existing goods but to process incoming ones through fetching their description and its analysis. For the scalability purposes the second generation of solution was moved to Apache Solr keeping previous successful NLTK-based NLP approach for text analytics.

Unicsoft involved highly-skilled Data Science and DWH experts for development and further tool support; break-even point for this particular solution was reached after 9 months of use through saving a significant amount of money by the cost-cutting on the staff that previously did such activities manually. The Client also was provided the support team for this particular solution for keeping all of analytical models up-to-date alongside with the highest level of accuracy.

The Client noticed high quality of Unicsoft delivery process as well as overall solution quality and expressed a willingness for further collaboration.



  • Service type: Dedicated data science team
  • Business domains: Finance, Insurance, Automotive
  • Expertise: Bigdata Processing and Analysis, Predictive Analytics, Machine Learning
  • Technology and tools: Python, XGBoost, Neural Networks, Decision Trees

Insurance industry is a highly competitive market which requires its players to constantly improve their value proposition. However, simple short-term strategies such as reducing prices and rendering discounts may lead to huge losses in a long run. Therefore, in order to be successful in the market, insurance companies have to be more and more creative in building and perfecting robust and precise models for proper insurance rate calculations and risk assessments.

Being aware of successful cooperation between Unicsoft and one of Lead Automotive Dealer, an International Insurance Broker (further referred as IB) contacted Unicsoft with an idea to develop a solution for a data-driven assessment. Being a partner of several automotive dealers, IB has an access to data received from in-car gauges and in-car smart devices. Therefore, there are two sources of available data: historical and descriptive data about users and the above mentioned data coming from devices, which build the foundation for increasing accuracy of the final model through determining a driving style and User’s proneness to an accident. All the abovementioned components were evaluated by Unicsoft as being crucial in terms of their impact on the final model and insurance rate respectively.

Key points of solution for IB were defined as:

  • choice of proper solution for handling streams coming from two data sources (internal from IB and external from ADs);

  • data cleaning, noise and outliers reduction;

  • development a Scoring model for risk level and proneness assessment;

  • machine learning and model improvement within a specified period of time after its launch.

Based on the identified solution parameters and goals Unicsoft set a team of Advanced Analytics experts along with DevOps and Data Warehousing specialists, which completed initial data cleaning and predictive model within two months and a deployment-ready version of solution within seven months; further, a support team was set up and introduced to IB.

The solution was successfully launched and had a commercial success with reaching the breakeven point within the first 18 months. The solution generated additional sales by offering a personal value proposition due to implementation of multi-feature risk assessment model and proper approach of rate evaluation for a particular User.

Having finished specified Model, IB underlined a very high level of Delivery process provided by Unicsoft, as well as a successful launch of the support team. Consequently, Unicsoft established a partnership with IB and was requested for development of yet another set of models outside Automotive domain.



  • Service type: Dedicated data science team
  • Business domains: Gaming, E-commerce
  • Expertise: DWH, Big Data Processing, Machine Learning, AI, Fraud detection & prevention
  • Technology and tools: Apache Spark, Hadoop, Hive, Python, mllib, Random Forests

Online gaming industry remains one of the most profitable domain of online businesses. However, alongside with huge demand, the huge fraud risk grows from year to year, causing not only a financial damage but far more dire consequences due to obtaining of hacked personal data. Having strong fraud protection is the key perk regardless how sophisticated and well-versed your game is. A well-known online game producer that launched the line of browser games faced a need to maximize monetization of one of his games through a thorough analysis of gamers’ data (purchases, activities, etc.) as well as need to make internal game security stronger due to up-to-the-minute detection of fraud and cheating activities. Despite having an analytics team, a vast majority of data was processed manually and data literally was not bringing any added or business value. So, the key intention was to build two analytical modules that supposed to work separately, but in the same environment and ecosystem; yet another intention lay in extensive advisory services provided by Unicsoft about the auditing current one and later, in setting up suitable and flexible DWH/ETL solution.

As a result of Business Analysis stage, Unicsoft identified following deliverables as required to fulfil set business goals:

  • Gaming analytics: given a historical data for entire game users and after its preprocessing (cleansing, enrichment, etc.) a cluster of active users has been defined. Then, key features of gaming behavior and in-game purchase occurrences were carefully analysis with the purpose to tune up gaming AI: main thing was to keep balance between game difficulty for keeping gamer involved and adding several “hard spots” which might be passed easily if particular in-game purchase made.

  • Anti-fraud module: analysis of suspicious activities occurred during gameplay or internal currency purchase: detecting fake cards and adding them into database; detection and consequently prediction of cheating activities in game process (multiple accounts, fake teams, etc.)

  • DWH/ETL auditing and improvement: regardless of having plenty of various datasets, all of them were stored in old and rather unstable SQL-type solution, so need of rapid access arose eventually; after thorough analysis of requirements, Clients’ data was successfully transferred to BigData solution that consisted of Hadoop + Hive alongside with analytics modules plugged onto.

Unicsoft set up a team comprised of senior-level data scientists, data warehouse specialists and business analysts with specialty in BigData field; further, a support team for model tuning and maintenance was set up as well.

The breakeven was reached after 12 months due to both modules: whilst almost right off the bat anti-fraud module gave a hand by dealing with cheaters, analytics module gradually helped in AI improvement and launching of particular in-game purchase sets dedicated to most promising groups of users eventually leading to significant increase in profits. Consequently, it led both of us to mutually beneficial cooperation.



  • Service type: Dedicated data science team
  • Business domains: FMCG, brand management, customer response prediction
  • Expertise: Bigdata Processing and Analysis, Advanced Analytics
  • Technology and tools: R, Python, Tableau

Knowing your brand and having a hang of how to promote, develop and embrace new technologies always was the key to success. So, a lot of companies strive to make data science assist them for increasing brand value and getting better knowledge of the customer.  One of the FMCG leaders, Large tobacco company, approached us with a  need to expand their brand-line alongside with increasing the number of distribution outlets. The Initial and foremost goal was specified as prediction of customer response to various marketing collaterals and design elements, i.e. different advertisement types, external branding elements such as outlets, big boards, and even colour combinations used in visual materials. Large tobacco company possessed a significant amount of historical data comprised of sales data and description of branding action of a particular moment. The final model was supposed to be complex and intended to detect any response within the predicted deviation of customer’s behaviour or purchasing activity which might have been caused by promotional and/or advertising activities.

Having considered the Client’s current and future needs, the key points of the solution were defined as:

  • fine-tuning of current Client ETL solution;

  • multicomponent predictive model containing several sub-models;

  • price elasticity and sensitivity model;

  • embedded Bi-solution showing customer’s response to any applied activities in both short-term and long-term time frames.

In order to implement outlined solution Unicsoft involved SME (Subject Matter Expert) to help building more precise model by identifying all dependencies and nuances in the Client’s business domain. Alongside with, a team comprised of Data Scientists and BI experts was set. Considering constant incoming data inconsistency and, subsequently, its effect on the model accuracy, Unicsoft initiated setting up of support team to continuously monitor and fine-tune model and maintain high accuracy.

The initial effect of the model became noticeable and appreciable within first four months. In addition to that, due to the complex analytical model for new activities prediction and doing business considering its “advice”, Large tobacco company managed to have a solid increase through customer response rate and, consequently, a revenue increase.

Large tobacco company pointed out a remarkable level of Delivery as well as high level of overall performance provided by deployed solution.



  • Service type: Dedicated data science team
  • Business domains: Media, Entertainment, Recommendation engine
  • Expertise: Bigdata Processing and Analysis, Machine Learning, Predictive Analytics, Recommendation Engine
  • Technology and tools: Python, Scala, Apache Spark

One of the leading Software Development Agencies (SDA) from the UK contacted us for cooperation on behalf of one of its Clients, who performed activities in the Content Media Market. The end-client successfully launched a platform similar to 9gag and Reddit alongside a mobile application. The Client’s business was focused on enhancing platform monetization by increasing the end-user’s loyalty and willingness to click & go to the partners’ offers. The only way to achieve this was to provide content which would be interesting, relevant and eye-catching for the end-user and could keep him engaged for a prolonged time period. However, currently, the Client seeks ways to extend technological man- and brain-power support and he turned to experts in face of SDA.

Both parties treated this project as crucial and mutually-beneficial. From SDA’s point of view it was challenging because of the scarcity of the Big Data experts market and the level of complexity in finding a solution without attracting external experts. From the Client’s point of view the issue consisted of emerging amounts of the users’ data, as well as a need in developing a strategy-related decision facilitated by grounded analytical results in order to increase the platform growth and market expansion.

Being aware of successful cooperation between Unicsoft and its partners, SDA chose Unicsoft’s service to perform the above task. Moreover, SDA underlined that Unicsoft was chosen in particular due to its robust Big-Data expertise in various domains, and also the company was reflected in a successful track record of collaboration.

The high-level goal was to build a recommendation engine which would use Big-Data analysis and machine learning to analyze individual end-users’ preferences and to help discover new content accordingly.

The project comprised two substantial parts – setting up the dedicated team of experts for continuous collaboration and transforming business needs into added-value solutions, in addition to direct development of the aforementioned. Unicsoft allocated two in-house experts, as well as quickly engaged one pre-interviewed expert from the HR pool. The proposed solution consisted of researching and developing two different Recommending Engines, each of which used different approaches (content-based and user-based).

As a result, SDA met all the deadlines for this Client, having brought the highest level of delivery and technical expertise. Client, respectively, managed to increase its audience monetization crucially (for the first six months after the deployment, click-and-go for partners offers grew by 20% compared to the previous period), whilst the overall audience increased by approximately 8%. As reported by the Client, the breakeven point was reached in five months after investment into the solution. SDA underlined yet another successful project with Unicsoft, and expressed an intention of further cooperation.



  • Service type: Dedicated data science team
  • Business domains: Automotive, Advertising
  • Expertise: Bigdata Processing, Predictive & Advanced Analytics, Machine Learning, IoT
  • Technology and tools: Apache Hadoop, Hive, Cassandra, Python, Scala, mlLib

One of the Leading Automotive Dealers (further – AD) that has been performing its activities since the 1990s in CIS and Eastern Europe is looking to increase the company’s revenue by exploring new monetization opportunities. The idea is to use the data collected from smart devices installed into a car for Target Advertising and to increase additional revenue stream through presenting precisely targeted, high-quality ads to car owners and sharing added value with selected DSP and SSP platforms.

AD’s internal analytics team required additional expertise which was not available at the moment, specifically: expertise in complex BigData processing and building SaaS solutions for IoT and Advertisement domains. Unicsoft was able to offer the required expertise which laid grounds for further long term collaboration.

As a result of Business Analysis stage, Unicsoft and AD defined key points of the solution:

  • auditing the current DWH & ETL solution, and identifying collectable data and key characteristics about it;

  • gathering data from GPS, smart devices and other gauges that could provide user data;

  • clustering the user data based on key features;

  • classifying users and providing initial ballpark prediction of users’ response to target advertisement;

  • analyzing historical data on users’ responses to ads and using machine learning techniques to enhance responses’ predictions over time.

In order to implement the solution, Unicsoft, set up a dedicated team comprised of Big Data and Predictive Analytics experts who provided further mode improvement, support and consultation in the deployment process of this solution as a stand-alone solution for AD.

Unicsoft successfully implemented and deployed the solution which allowed AD to build additional revenue stream in cooperation with several DSP and SSP platforms; R&D investment carried out by AD reached the breakeven point within 12 months.

The prediction of car owner’s response lead to lower than expected number of opt-outs, positive response of car owners to new service and high advertising conversion rates.

Given these projects are completed, AD indicated a high level of delivery, R&D value and top-notch class of specialists involved. AD encouraged Unicsoft on further partnership that led to another Data Science initiative in Automotive Insurance domain.



  • Service type: Dedicated data science team
  • Business domains: E-commerce
  • Expertise: NLP, Text Mining, Data Science
  • Technology and tools: Python, NLTK, NLP

One of well-known Central Europe e-commerce merchants asked Unicsoft for cooperation to create a tool intended to analyse a customer’s feedback on goods purchased through web marketplace, The business goal was to increase customer loyalty, drive business changes, and deliver real return on investment. Customer experiences fall into three basic categories, positive, negative or neutral. Through sentiment analysis, the goal was to detect the tone and temperament of each and every word found in a customer’s social postings and categorize those sentiments as either positive, negative or neutral.

After data cleaning and munging , an initial step of words tokenization was applied; having it done, an NLTK tools was used to define synonyms, semantics, and overall mood of feedbacks; aligned with scores given by these particular feedback authors, a section of manual business logics was brought in: language specifics, abbreviation, collocations and vernacular expression played a significant role in overall semantic analysis. Furthermore, analysis of new products or product lines could significantly affect overall strategy. Alongside with NLP and semantic analysis, there were used a Data Science techniques : given a data from social network that customer logged in through, a set of demographic features was involved to model, summing up into complex analytical solution.

Unicsoft set a dedicated team comprised of highly-skilled analysts and mathematicians and software developers; this particular solution helped the Client to define his marketing and sales strategy which resulted in 10% revenue increase within one year after the deployment. The Client noticed the overall high-level of Delivery and solution architecture.



  • Service type: Dedicated data science team
  • Business domains: Commerce, Pharmacy
  • Expertise: Data Science, BI, Predictive analytics
  • Technology and tools: Python, noSQL, xgBoost, Tableau

One of Unicsoft clients, a large pharmaceutical network came up with request for a collaboration regarding development and implementation of OOS (Out of Stock system). For that current moment, Client had over 2000 drugstores all around the country and a reporting module comprised of reports concerning revenues and daily turnover.

The main goal was to set up a proper ETL process and perform a predictive analytics module intended to predict absence of drugs & remedies alongside with BI-tool deployment for ad-hoc reports and predictive analytics. Subsequently, the request evolved into not only a analytics & reporting module, but a complex solution providing access for suppliers to let them see the picture considering sales & trends of their products in any point of sale.

Unicsoft started from setting a dedicated team comprised of data architects and engineers to establish and consequently maintain data migration process. Considering necessity of being able to do close to-real-time analytics as well as requirements of DB consistency and availability, a noSQL architecture was considered as the most suitable. Thence, a several data scientists were involved to manage data enrichment and munging process. Having this step completed, a predictive analytics module was implemented to calculate estimated risk of product absence and predicting sales trend. As a copestone, a BI module was added providing a visualization of predictive module outputs.

Client underlined well-organized and orchestrated delivery process from Unicsoft side, and, consequently, this case of collaboration led us to establishing a successful cooperation along with new projects for pharmaceutical industry.


Lending platform for UK client

  • Service type: Fully-managed project development
  • Business domains: Finance, Microfinance
  • Expertise: Blockchain, Predictive analytics
  • Technology and tools: Angular.js, Python, Django, Blockchain API library 1.3.3 & 1.4.0, MLlib, pandas. Docker

Lending services have been occupying leading positions among all services for a long time. During the existing lifespan this market remained rather pristine in terms of technological approach. However, with the emerge of Blockchain, lending services stepped up to embracing of its benefits. For this industry, in particular,  – removing an intermediary and securing any cashflow within the system. The sooner company applies and implements this, the more superior it gets, so, our Client decided to extend their audience due to to novelty and security of the product. One of the peer-to-peer lending platforms, operating in the UK & Ireland and regaled as an one of the leaders in industry asked Unicsoft to provide development services. Unicsoft was selected as an authorized partner for creating an entire platform using the old one as a basis. The key peculiarity of aforementioned system was the use of Blockhain technology. Client wanted to implement smart-contract system that would allow Users to exchange secured contracts alongside with opportunity to request & receive loans in Ethereum as well as in more “accustomed” currencies.

  • The client wanted to build robust platform intended to provide fast and secured operations set;
  • Provide maximal security options for the User’s personal and transaction data;
  • Shift from the traditional monolithic architecture to the microservice architecture allowing fast scaling – up and to increase its resilience;
  • Create cross-functional user-friendly UI which ensures flexibility and handy, comfortable work with multiple smart-contracts/peers;

Maximize the proliferation of Blockchain-based operation both in long and short runs, starting since the very first release of the platform. The main intention was defined as to provide gradual but comprehensive pivoting to the being of a full Blockchain and Cryptocurrency leading platform and slightly casting aside traditional peer-to-peer lending in a long run.

First and foremost, the team created the microservices architecture, removing the old monolithic structure, providing much more flexibility when it comes to upgrading as well as maintaining separate parts of the solution. The main challenge, besides sorting out the components of the chosen architecture was to pick a proper technology stack. After thorough comparison of many options, the final choice lay between C# and Python. Accounting the fact that Client expressed his desires to have a cross-functional platform which would stand aside of any platforms, the choice was obvious and C# was ruled out, Then, having the set of architectural pinnacles defined we kicked the development off.

One should mention that all of the Blockchain-related frameworks were successfully integrated to the system, particularly, to its core. As far as an adherence to microservice paradigm was chosen, we were quite indubious regarding framework for UI, and perceiving both compatibility and resilience we chose Django.

Another very important part that one should not be oblivious of was a security. As far as Client expressed that he would like to see a highest level of the data protection (both transactional and personal), this remark was considered and taken care of which led to inclusion of a ISO 27001 standards to the Smart-contract system.

Then a full-scale platform was developed within 12 months work. Aforementioned microservices architecture ensured that all security measures would be implemented alongside with protection. The frosting on the cake was an implementation of the scoring model, which provided risk assessment and suggested whether the User is likely to be approved for a loan. Abovementioned system grounded on MLLib library plugging-in, and represented a multi-factor approach to a risk assessment and its further maintenance. In a nutshell, when it was coming to the decisive point. like “should I lend this man my Ethereum/Euro”, a little window popped up, showing a personal User credit score based not only loan history but on the personal data.

The platform was successfully launched and reached its breakeven point within 6 months, providing the Client with the growing Users portfolio and maintenance of financial risks thus positively affecting the revenue. Portfolio, in its turn, accreted to 115% within three months after launch compared to a same period of the previous year.


Prediction of a bill-voting outcome

  • Service type: Fully managed project development
  • Business domains: Media, Social
  • Expertise: Behavioral analytics, Predictive analytics
  • Technology and tools: Python, SQL & noSQl, R, mlLib, Spark

As long as Data Science technology stranding along the market it is covering more and more domains where it can be applied. Applying Data Science to the domain where not only behavioral analysis but decision pattern matters and affects the political and social outcome became sought-after. One of the most renowned EU&USA publishers reached out to us with an intention to build a new value-added service for their subscribers focused on prediction of a bill-voting outcome in US congress. Such service was offering an innovative, impartial and accurate approach to political analytics, as opposite to classic “expert” approach used by other players on the publishing market.

The journey began with deep analysis of the problem and available data to solve it. The main challenge was that in order to get high accuracy the machine learning model had to gather attributes of a bill and voting habits of senators from multiple non-congruent sources. Added complexity was the fact that both “bill” and “voter” are complex entities and specific voter decision regarding specific bill depends on multiple attributes and their combinations, therefore requiring advanced approaches for prediction.

Initial development team consisted of two data scientists and one DevOps. First, we built an API-like connector for grabbing and processing the data. A lot of work was dedicated to clean up existing data – all success of the model hinged on apposite data cleansing and enriching. Moreover, considering potential growth of external sources, current data processing module must have easily expendable architecture for rapid scale up once we have new source on the board.

Next step was dedicated to getting a full understanding of the data thus we ran a set of models to get descriptive statistics and full comprehension of the given data. Having the latter completed, we did the PCA (Principal Component Analysis) to understand the weights and how the key features do affect the outcome. After the all of abovementioned, we devised a plan of model testing – starting from 10 “competitors” we boiled a list down to the four key models and amalgamated them into an ensemble.

Long story short – the model had an accuracy of 84% proving the business case. Such successful proof of concept initiated series of new initiatives and value-added services based on the data existing in the organization and utilizing the power of data science and machine learning to gain unique insights from it. This solution became a capstone for a versatile solution allowing users splice&merge datasets and predicting not only bill-voting outcome but a precursors leading to the decision of even factors, affecting behavioral pattern of a particular congress member.



  • Service type: Fully-managed project development
  • Business domains: Finance, e-Commerce
  • Expertise: Machine Learning, Data Security
  • Technology and tools: R, Python, mlLIB

We live in the “era of data”. Everyday companies face a compelling number of interactions with data source, and the more data is being produced, the more risks of this data to be used maliciously arise. Our Client, European start-up had an idea of developing a solution for the forecasting of spear-phishing of personal data for a wide scope of organizations located in various countries. This type of solution is excessively sought-after nowadays  – especially combined with ML approach for striking prediction accuracy. Knowing about Unicsoft expertise in that domain and having previous successful track record with us, Client decided to choose us as an authorized solution developer.

Firstly we defined main datasources: the pinnacle was VCDB database (catalog of data security incidents using VERIS framework). Second source used in the analysis is FT500 data set in 2016. Then, after merging these two data sets we obtained set for predicting data breach probability for a particular company. Key ML models were GLM and Random Forest models with RF prevailing because of its higher precision. Moreover, after implementation of Monte-Carlo simulation methods we added prediction of incident density within particular timeframe. As an additional perk we predicted expected loss in USD in case of attack for various industries.

Solution was accepted positively right after its demonstration by various investors and got a lot of encouragement for the further development into more complex product with extended functionality. Due to model precision that amounted to 83% and the fact of Monte – Carlo simulation techniques implementation one may be confident that abovementioned solution has a huge potential and versatility.

Contact Us
[javascript protected email address]
Aleksey Zavgorodniy, CEO, Tel: +1 650 515 36 99
[javascript protected email address]
Julia Liubevych, CBDO, Tel: +1 650 451 11 06
[javascript protected email address]
Vyacheslav Basov, CTO, Data Science, Tel: +44 753 344 070 63
[javascript protected email address]
Alyona Zhurba, Account manager, Tel: +44 131 208 08 07