Solutions

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Assess

Rigorous scoping and assessment of business critical projects at an early stage can often make the difference between future success and failure. Clearly understanding whether your business has the tools, people, data, infrastructure or budget to achieve your vision allows you to anticipate and plan accordingly.

Years of expertise and numerous successful business transformations allow our experts to advise on the best approaches, technology and implementation plans to define and underpin your vision.

Design

It is impossible for most organisations to create or retain a dedicated R&D team of visionaries with the breadth and depth of academic rigour and cross-disciplinary expertise for profound research.

Our researchers provide an on-demand ability to provide you with access to the best and brightest thinking and new break-throughs from our academic network across the globe.

Read DESIGN Solution Case Studies

Blockchain Development: Detect rogue trading using the Blockchain

Rogue Trading
Rogue trading is an issue that has plagued multinational banks since their inception. Defined as a trader who acts independently of others. The banking industry as a whole has lost upwards of £10 billion due to rogue trading, the most recent of which was Jerome Kerviel who lost £3.7 billion in 2008. In some cases, these losses have brought down whole banks as is the case with Barings Bank and Nick Leeson.

Examples
Societe Generale – £3.7 billion
Barings Bank – £827 million
Allied Irish bank – £697 million
Daiwa Bank – $1.1 billion
Sumitomo Corporation – $2.6 billion

How it Happens
Rogue trading occurs when a trader covers up trades by booking a trade that hedges directional risk. Typically, the trading will “book” these hypothetical hedge trades so that they can cover up large positions. If these positions go south, the bank can stand to lose a lot of money. The compensation structure means that there is an unlimited upside (larger bonus) for the trader but limited downside (getting fired).

How it Can Be Prevented
Policies and strict governance can prevent rogue trading but another more direct way is stricter controls on the technologies used for booking these trades. Inefficiencies and inconsistencies in databases used within banks may allow traders to hide these trades from the central risk book. DataSpartan worked on a Blockchain proof of concept platform that addressed these database inconsistencies.

How Does Blockchain Help
A Blockchain type solution allows different departments within the bank to have a consistent view of orders – this is because it is impossible to delete or change any of the executed trade orders without other departments agreeing. This provides transparency about trade execution and minimises the chance of rogue trading.

Our Results
The proof of concept is now being fully integrated into a subset of the servers in the multinational bank for live testing. APIs have also been built to allow developers to interface with Blockchain functions.

Lead Classification: Clean And Standardised Analysis

Client Classification
For business to business enterprises, client classification focuses on putting a customer into a specific category or bucket. In this specific scenario, DataSpartan was working with a crowdsourcing startup who wanted to identify whether or not a business on their platform was likely to “hit” their crowdfunded target amount raised. The client also wanted us to inform them of which variables were most important in terms of increasing the likelihood of a raise.

Problem
A large proportion of the onboarding process was done manually and it was extremely time consuming for the startup to fully onboard each client only to have them not complete a successful raise. Standardized data was collected about the size of the company, the number of followers they had in social media as well as financial performance, their team, industry and competition as well as exit potential. Currently, 56% of the companies that were going through the funnel had a successful raise.

Solution
The first part of the problem was identifying which variables were the most likely to influence the outcome. Historical data was extracted, cleaned and standardised to allow this analysis and three variables were singled out which determined the probability of a successful raise. A supervised learning model was then created which could classify these leads based on their likelihood of conversion based on their data. Python was used for this portion of the data analysis and libraries such as NumPy and SciPy were leveraged to ensure rapid computation and fast model prototyping.

Our Results
The current research and results have been incorporated back into their lead pipeline in the form of a qualifying questionnaire which filters out unlikely leads and pre categorises the clients. This has resulted in a 4% decrease in the number of man hours used as well as a 6% increase in the number of companies that successfully raise. Further work is being done to automate some of the manual processes such as KYC (Know-Your-Client) document verification to allow the client to efficiently scale up its business. Work is also being done to improve the data pipeline and to ensure that the data collected is being stored in the appropriate format for analysis.

Trade Analysis: Experts In Medium & High Frequency Data

Price Prediction
Since the start of the electronic market, price prediction has always been a hot topic. With the advent of machine learning, the field is becoming more and more contested with mathematical methods leveraging these new tools. Stories from hedge funds such as Renaissance Capital have inspired financiers around the world to look towards mathematics as a means of generating alpha.

Data
Two quants from DataSpartan were assigned to work on and analyse medium frequency and high frequency data respectively. They were asked to specifically investigate bond and option prices over a one hour period and a millisecond period to determine if there are any patterns that could be discerned from the data. The client had a proprietary data feed of tick data for each symbol which allowed the quants to conduct in depth research.

Approach
The client had a preexisting framework written in Java which could instantaneously switch between backtest and live. Ideas were gained mainly by reading research papers in the physics, biology and mathematics fields – the ideas were then explored and implemented on time series data. Strategies had to be run for a minimum amount of time and deemed profitable in a backtest before being put onto live servers. Concepts which had predictive characteristics such as entropy were explored (entropy has been used to predict the probability of cardiac arrest during open heart surgery by investigating the differences in systolic and diastolic pressure).

Our Results
After four months of testing, a strategy was found that yielded a back tested Sharpe ratio (a measure of the reward over risk) of 1.5 inclusive of commissions. This strategy is now being run live on the platform and is currently generating profits for the client. Work is currently being done on the strategy to check if it works on other instruments in different asset classes. Due to the success of this strategy, the client has has DataSpartan to do some further work investigating strategies for portfolio allocation and diversification on a macroeconomic level. They want to investigate the automation of Warren Buffet’s trading style by analysing 10-K reports using machine learning and determining the optimal allocation of capital on an industry basis.

Stock Price Prediction: Develop an End-to-end System for Price Analysis

Purpose
Our client lacked the in-house talent required to develop a generic trading platform for back testing strategies. DataSpartan was brought in to resource and manage the project.

Approach
The backtesting system was built in Java and used Spark Streaming, Kafka and HDFS to build a continuous exchange-transform-load (ETL) pipeline for gathering the data feeds of stock prices and in particular streaming social media data in order to produce short term predictions of the stock price.

Our Results
The system has been fully built and currently DataSpartan quants are building on top of the system in order to identify profitable strategies in both the high frequency and medium frequency space for market making. Currently, a market-impact tested strategy of Sharpe ratio 1 has been found and is being put into production.

Build

Creating the business case internally and externally is never more powerful than a working prototype. Seeing dataflows, systems, software and new processes working together in harmony generates interest, buy-in and financial investment for bigger and more impactful projects.

Our cross-disciplinary experts deliver prototype projects rapidly without generating disruption to BAU on already overstretched internal teams. The flexibility to deliver extremely complex projects or products across any industry allows corporate or start-up leaders to clearly demonstrate demand, impact and ROI around their vision.

Read BUILD Solution Case Studies

Risk Management: Instruments Of Protection

Options Risk Management
Large multinational banks often use a variety of instruments in order to protect themselves from risk. One of the most common ones is options. These are contracts which allow the buyer the right but not the obligation to exchange the stock at a specified price on a specific date. These are used typically to manage risk and ensure that the bank is not overexposed to market risk in any specific sector.

Detecting Market State
The type of tool used for hedging risk is dependent primarily on the state of the market. The client we were working with wanted to use reinforcement learning – the same type of learning that was used in game engines such as Alpha Go. They wanted an algorithm developed that could detect the “state” of the market and prompt traders to act accordingly based on historical data.

Variable Selection
A large number of variables were taken into consideration for this problem such as the stock price, momentum, earnings releases and 10-K reports, this data was collated and aggregated into a standardised format. At the industry level, these companies were grouped and statistics were calculated such as the average volatility of a stock in that industry. Integration was then done with a live Bloomberg price feed to give traders an indication of the state of the market – bullish or bearish for a given stock based on this information.

Solution
An automated hybrid solution built on top of Python was created to allow the client to anticipate unusual movements of the market would allow them to better provide their services and manage the risk associated with it. Interactive graphical tools were built in Java Swing which integrated with these Python services in order for the floor traders to better assess the best course of action to take in a specific scenario.

Our Results
DataSpartan delivered both research results for this case and an interactive visualisation of maker movements to their requirements. This tool allows them to better anticipate shock and explain tendencies. The current tool is integrated with on the dashboards of options traders as a customisation widget – documentation was also provided to the client in-house development team in case they wanted to expand it further.

Document Search: Financial Institutions Algorithm Search

Search
Since the advent of Google, search has been the bedrock for the spur of the information age. With the touch of a button it is possible to access any public indexed document stored. The drawback with conventional search engines such as Google and Bing is that they cannot be used on proprietary data sets.

Compliance
In the context of compliance, financial institutions usually have a compliance team monitor and overlook other teams to ensure that they are not in breach of any regulations. In the case of the client, they wanted to save time by ensuring that their compliance team was able to find the right documentation. The client had an internal database of PDF files which contained information about whether or not a statement or comment should be flagged.

Phrases
There are certain keywords and phrases that traders would use that corresponded to a specific set of stocks. This would amount to insider trading and the job of the compliance team was to closely monitor these Bloomberg chat logs to ensure that this was not the case. There were a very high number of false flags which meant that the team had to spend time trying to identify the relevant document to check if it was a real flag.

Solution
DataSpartan was asked to solve the search part of the problem by creating a tool which would save the compliance officers time by allowing them to search their internal databases more quickly for the relevant information. A custom interface was built using Django which had PDF previews for key words and phrases to allow the officers to preview the documents manually and by eye for relevancy.

Our Result
This is the first component of a larger system which involves a document recommendation engine highlighting the usage of the keywords in the document explicitly. The component being developed is currently being integrated into the client servers and is projected to save each officer 3 hours of work each month. Full integration of the larger system is projected to save 10 hours of work each month.

Lead Classification: Clean And Standardised Analysis

Client Classification
For business to business enterprises, client classification focuses on putting a customer into a specific category or bucket. In this specific scenario, DataSpartan was working with a crowdsourcing startup who wanted to identify whether or not a business on their platform was likely to “hit” their crowdfunded target amount raised. The client also wanted us to inform them of which variables were most important in terms of increasing the likelihood of a raise.

Problem
A large proportion of the onboarding process was done manually and it was extremely time consuming for the startup to fully onboard each client only to have them not complete a successful raise. Standardized data was collected about the size of the company, the number of followers they had in social media as well as financial performance, their team, industry and competition as well as exit potential. Currently, 56% of the companies that were going through the funnel had a successful raise.

Solution
The first part of the problem was identifying which variables were the most likely to influence the outcome. Historical data was extracted, cleaned and standardised to allow this analysis and three variables were singled out which determined the probability of a successful raise. A supervised learning model was then created which could classify these leads based on their likelihood of conversion based on their data. Python was used for this portion of the data analysis and libraries such as NumPy and SciPy were leveraged to ensure rapid computation and fast model prototyping.

Our Results
The current research and results have been incorporated back into their lead pipeline in the form of a qualifying questionnaire which filters out unlikely leads and pre categorises the clients. This has resulted in a 4% decrease in the number of man hours used as well as a 6% increase in the number of companies that successfully raise. Further work is being done to automate some of the manual processes such as KYC (Know-Your-Client) document verification to allow the client to efficiently scale up its business. Work is also being done to improve the data pipeline and to ensure that the data collected is being stored in the appropriate format for analysis.

Stock Price Prediction: Develop an End-to-end System for Price Analysis

Purpose
Our client lacked the in-house talent required to develop a generic trading platform for back testing strategies. DataSpartan was brought in to resource and manage the project.

Approach
The backtesting system was built in Java and used Spark Streaming, Kafka and HDFS to build a continuous exchange-transform-load (ETL) pipeline for gathering the data feeds of stock prices and in particular streaming social media data in order to produce short term predictions of the stock price.

Our Results
The system has been fully built and currently DataSpartan quants are building on top of the system in order to identify profitable strategies in both the high frequency and medium frequency space for market making. Currently, a market-impact tested strategy of Sharpe ratio 1 has been found and is being put into production.

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Transform

Major infrastructure change or business critical evolution from legacy systems is one of the most complex, risky and expensive strategic moves any business leader can make.

Our experts act as a talent-multiplier bringing many disciplines to bear at once, speeding up delivery, making complex workflows simple and installing new platforms and data-flows whilst ensuring the impact is game-changing and long-lasting for your business.

Read TRANSFORM Solution Case Studies

Transform Solution Case Study
A global financial institution approached DataSpartan to improve the ingestion into their data lake of billions of daily credit card transactions.

The data lake was shared across multiple different business units, each with different use cases, requiring real-time, self-service functionality, not possible within its existing legacy platform.

The client’s technical leadership team had expected that such a complex transformation in architecture and new systems would take a team of 20 normal vendors approximately 12 months to complete.

DataSpartan deployed 2 experts who completed the project in the same time frame. Not only did they configure the base code to provide long lasting resilience, but optimised and reduced computational consumption from 20 nodes down to 3.

In addition, DataSpartan proposed and rolled-out a new operational system to manage all end-to-end processing ensuring the overall architecture was cheaper to maintain and easier to build future internal products and reporting systems.

Anti-Money Laundering: Manage customer information

Purpose
Our client was a large multinational bank and regularly interfacing with the Central Bank. They required DataSpartan to create an anti-money laundering and know-your-customer system to manage customer information and predict customer credit risk.

Approach
An end-to-end system was created using J2EE, DB2, Oracle, and Biztalk. DataSpartan delivered the work in sprints and completed the project over a 6-month time period. In addition to the backend features, an extensible internal dashboard was created to provide a global overview of customer credit risk to management.

Our Result
The system is currently in use and the client is using it to generate customer credit reports for Central Bank compliance. Multiple manual processes were automated through this system.

Validate

Having invested time, energy, resources, people and money into building your product or new processes, how do you ensure that your own team has covered all bases and de-risked its launch, adoption and growth in your business or target market?

Our experts rigorously investigate, re-engineer, test and validate your code, architecture, data-flows, methodology, software and technology stack to highlight issues or improvements and underwrite success.

Rescue

Projects, large or small, often come off the rails with overspend or elongated delivery timescales as internal or external teams underestimate the scale and complexity of the tasks involved. Key people may leave mid-project, or the organisation might not have access to the expertise or experience required to solve the problems it encounters, stalling further progress.

Our experts specialise in deploying into seemingly impossible situations and rapidly unblock and complete projects that were deemed irretrievable or over-budget.

Read A RESCUE Solution Case Study

Capital Stress Testing. DataSpartan Testing Platform

Importance of Stress Testing
Since the financial crisis of 2008, banks have been under additional pressure to ensure that they are compliant with the regulations required for stress tests. The idea behind stress testing is to ensure that the bank has enough cash in reserve in the case of a catastrophic market failure. Specifically, banks are mandated to move to a VaR (Value at Risk) model to an FRTB (Fundamental Review of the Trading Book) model. This FRTB model is much more stringent and forces a review on the desk level of the trading positions.

Detecting Market State
The type of tool used for hedging risk is dependent primarily on the state of the market. The client we were working with wanted to use reinforcement learning – the same type of learning that was used in game engines such as Alpha Go. They wanted an algorithm developed that could detect the “state” of the market and prompt traders to act accordingly based on historical data.

Testing Platform
The introduction of these new rules meant that the client needed to have a system where they could run the FRTB tests required by the government – due to the complexity of such a system – it was important to not just understand the underlying mathematics behind the models but to also ensure that the testing tools developed were compliant with the requirements.

Difficulty of Implementation
Prior to DataSpartan taking over the work, the client had previously used another well known consultancy to undertake the project. Unfortunately, there was limited success with the platform built because they had used templates that were designed in a one size fits all and there were nuances and intricacies in the client systems that meant that a custom build was needed.

Our Approach
DataSpartan provided a custom build that was tailored to the nuances of the client data. In addition, other inputs had to be taken into account such as the Volker metrics in compliance with the Dodd Frank act. Custom APIs needed to be built to allow the in house developers to build team specific functionality on top of the platform. Because cross-team data was required, a standard data capturing tool was built to ensure that teams provided the system with an input that was compliant and made data integration into the calculation engine much easier.

Our Results
The client was satisfied with the work and is using the system internally for stress testing purposes. When it was finished, the system could properly report on tests on a daily and weekly basis as well as flag any activity that was unusual. The system was clearly documented and handed off to an offshore development team to assist with maintenance and feature implementation with DataSpartan still having oversight for technically difficult challenges.

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Contact

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business@datapartan.com