Finance in IT

We are requesting 20,000,000 euro to place 900 orders on the exchange. What could go wrong?

Today, I will explain how to avoid losing billions in client money when executing large transactions on the exchange. This discussion focuses on an often overlooked and largely invisible problem that arises when handling large requests, particularly HTTP requests, which may not be fully executed. It's surprising how little attention is given to this issue and how few tools there are to address it.

Our task was to implement large-scale management of exchange orders, not just within a single exchange but globally, and to ensure it operates reliably. In this story, you'll encounter clients, servers, and cats. Stories are always more enjoyable with cats.

Trading robots are conquering the Wall Street! Learn how to create your first automated python trading bot.

We present a fully functioning trading bot pipeline on python using the Binance API. Starting with the general introduction, we provided a comprehensive overview of main API calls and their implementation on python. After this we show a fully functioning python code presenting a basic trading bot with core features using static channel breakout strategy.

Hi Everyone,

I would like to draw your attention to some key insights from two comprehensive reports, shedding light on the importance of fraud prevention as a burgeoning field with substantial job prospects. The original reports can be accessed through the link provided at the end of this message.

Firstly, let's consider a couple of significant events from the previous year that remain highly relevant. Retail e-commerce sales surged to a staggering $5 trillion, with projections indicating a further increase to $8 trillion by 2026. Furthermore, the percentage of shoppers making cross-border purchases rose from 69% in 2019 to an impressive 76%.

These statistics affirm that online shopping is not a passing trend, as consumers are willing to make purchases from international vendors, even if it means enduring longer delivery times in exchange for lower prices. From a fraud prevention perspective, this highlights the escalating number of non-domestic payments and the rise in the use of lost or stolen cards for purchasing goods that can be resold in the future. Notably, refund fraud is becoming increasingly prevalent. However, many businesses have adapted their protocols to accommodate these emerging challenges. One such positive development is the gradual disappearance of restrictions on using payment methods that do not match the account country.

As cross-border purchases continue to proliferate, alternative payment methods are also on the rise. Current global statistics indicate that an average of four payment options are now available on merchant platforms. Additionally, open banking payment transactions are projected to surpass $330 billion globally by 2027, leading to a greater diversity of alternative payment methods. As companies embrace these methods, they are also becoming more aware of the associated risks. For instance, direct banking payment methods present unique challenges as they lack refund capabilities, while Buy Now Pay Later (BNPL) options introduce their own set of risks.

Hello, I am Aleksandr Ivanov, CTO at Quadcode. We came to Habr to show the Fintech “kitchen”; we’ve been at it for 8 years, so we can already share our experience. In our blog we will talk about architectures, technologies, tools and life hacks.

When you have an IT, which supports all aspects of your organization automated performance and you have a firm confidence that IT executes well, there is always a chance that something could go wrong in technology or in related IT processes. Depending on complexity of IT environment varieties of risks scenarios could arise. This article as a summary of different faithworthy sources aims to help you in getting high level understanding on what could go wrong and how you can predict it in a more conscious way.

My name is Maxim Tornov and I have been working in various IT areas for a long period of time. Since then, for over 14 years I am working in the area of IT/IS risk management with focus on in IT/IS audits, internal control implementation and assessment.

I am sure that at the present the topic of Information Technologies risk management became more vital. Organization’s efficiency in IT risk management directly affects the achievement of various organization’s goals, goals which have dependency on IT, those goals may include reliability and efficiency of business processes, the organization's compliance with regulatory requirements, the integrity of financial reporting, and many others.

I sincerely hope that this material will be useful to you and may give you some new ideas that you can contribute to the benefit of your personal development and the development of your organization's risk management culture.

Free money to fund your own business is probably the most cherished dream of every budding entrepreneur.

And getting a grant is what can bring it to life. While small business owners dream of a grant to fund their startup, the process of its obtaining is not as easy and cloudless as it may seem.

However, if you know where to look for the right fund and how to apply, one can significantly nick in the path to getting seed money.

Difference between a grant, attracted investment, and loan.

What is this article about?

In this article, I will tell about my participation in the first (out of two so far) Telegram blockchain contest. I didn't win any prize. However, decided to combine and share the unique experience I have had from the start to finish line, so my observations could help anyone who is interested.

Since I didn't want to write some abstract code, instead make something useful. I created instant lottery smart-contract and website which shows smart-contract data directly from Telegram Open Network (TON) avoiding any middle storage layers.

The article will be particularly useful for those, who want to write their first smart-contract for TON but don't know where to start.

Using the lottery as an example, I will go from setting up the environment to publishing a smart contract, interacting with it. Moreover, I will create a website that will show smart-contract data.

The last decades the world economy regularly falls into this vortex of financial crises that have affected each country. It almost led to the collapse of the existing financial system, due to this fact, experts in mathematical and economic modelling have become to use methods for controlling the losses of the asset and portfolio in the financial world (Lechner, L. A., and Ovaert, T. C. (2010). There is an increasing trend towards mathematical modelling of an economic process to predict the market behaviour and an assessment of its sustainability (ibid). Having without necessary attention to control and assess properly threats, everybody understands that it is able to trigger tremendous cost in the development of the organisation or even go bankrupt.

Value at Risk (VaR) has eventually been a regular approach to catch the risk among institutions in the finance sector and its regulator (Engle, R., and Manganelli S., 2004). The model is originally applied to estimate the loss value in the investment portfolio within a given period of time as well as at a given probability of occurrence. Besides the fact of using VaR in the financial sector, there are a lot of examples of estimation of value at risk in different area such as anticipating the medical staff to develop the healthcare resource management Zinouri, N. (2016). Despite its applied primitiveness in a real experiment, the model consists of drawbacks in evaluation, (ibid).

The goal of the report is a description of the existing VaR model including one of its upgrade versions, namely, Conditional Value at Risk (CVaR). In the next section and section 3, the evaluation algorithm and testing of the model are explained. For a vivid illustration, the expected loss is estimated on the asset of one of the Kazakhstani company trading in the financial stock exchange market in a long time period. The final sections 4 and 5 discuss and demonstrate the findings of the research work.

• Inter-bank transfer systems (see a research paper by Narula et al.)
• Privacy-focused management of digital assets (see a proof of concept by J.P. Morgan and zCash)
• KYC (see a proof of concept by ING)
• Self-sovereign identity (see an attribute-based credentials EU project)
• Voting (see a proxy voting prototype by Russian National Security Depository)