EagleAi Use Case Series: Fraud Risk – Undetected spoofing practice at a trading firm brings disrepute to the firm along with hefty fines

Many trading firms have been charged with insufficient supervision of trading activities of their own traders. Recently a large wall street firm was charged with hundreds of millions of dollars of fine for insufficient fraud detection controls that allowed spoofing practice in one of their trading desks to go on for years. Trading patterns such as spoofing, layering, front running etc. are considered fraudulent trading patterns. Trading firms are expected to have controls that detect such trading behaviors by both their own traders and by their clients. Fraudsters employ different techniques, often even using sophisticated technology like a trading algorithm to mask the fraudulent nature of their trading behavior.

In this use case scenario, we consider a hypothetical example of a trader wanting to buy a stock at a lower price than the current price of the stock.

This trader would try to move the market towards his/her desired bid price by placing a lot of sell orders a few ticks away from the best offer and creating sell pressure on the stock and thereby moving the price lower. This is illegal and should be detected by the trading firm using their fraud detection controls. Once the price is in their price range, the trader would cancel all their sell orders and execute their buy order. A similar case of spoofing can also occur if the trader wants to sell their position at a higher price and moves the market higher using spoofing orders.

In this hypothetical example, trader has applied spoofing techniques to profit from those trades. Eventually the regulatory authorities caught up with the traders and found them guilty of fraud and fined the trading firm a large sum as fines and restitution for the losses they caused for other investors.

EagleAi Fraud Risk module looks for patterns of fraud from transaction logs, such as spoofing and layering on trading activities of clients and traders. It looks for patterns of buy orders and sell orders placed by the traders on every symbol and check if they follow the fraudulent trading patterns. If it detects such patterns, it alerts the risk managers with contextual data to aid in further investigation. Timely report from EagleAi would help the risk managers to identify such issues early on that could lead to taking corrective actions at the earliest. This not only prevents leakages of hundreds of millions of dollars as fines but also prevents the firm from any reputational damages.

EagleAi – The Fraud Detector with Eagle vision.
EagleAi Fraud risk detects fraudulent methods like Spoofing, Layering and Front running techniques to avoid costly fines and litigations.
Book a demo today @ https://eagleai.com/tradewatch-fraud-risk/

Check out our complete product suite – TradeWatch Trading RiskTradeWatch Compliance RiskTradeWatch MarketData RiskTradeWatch Position RiskEagleAi Anomaly DetectorEagleAi Trend Detector

EagleAi Use Case Series: Market Data Risk – Bad market data causes a broker to reject orders from a client incorrectly which leads to a financial loss

In electronic trading, quality of market data is very critical. There has been too many instances where trading firms’ algorithmic engines trading on bad market data has caused huge losses for the trading firm. There are compliance rules that require brokers to ensure the correctness of an order by applying pre-market limit checks. It is important that these checks are applied with correct market data. In this blog we are going to consider a realistic but a hypothetical scenario where bad market data could cause huge losses even when it prevents a legitimate trade from happening.

Scenario: Trading systems subscribe to real time market data which has got bad market data for delta, gamma and other greeks of an option for one of its symbols.

  • Delta is 20x the usual delta for an option with a similar strike and contract expiry month. The limit checker checks the “Delta Adjusted Notional Value” of a client’s option order and rejects the order as it computed a 20x a typical value of the delta adjusted notional value.
  • Because of the limit breach, client is not able to send any orders to the exchange through the broker. It takes a while for the support team to figure out what is happening while the client is restless to place the orders.
  • When knowing about the trading glitch, client is upset with the broker.
  • Broker makes the client whole by agreeing to pay more than $100,000 for the trading loss suffered by the client during the 30 minute period it took to resolve.

It is obvious that a very high quality market data is critical to any trading activity at an institutional scale. When algorithms make trading decisions, it is even more important to have high quality market data. Despite the efforts of the team that supports the market data infrastructure, erroneous data does slip in. EagleAi can be an effective engine to aid in flagging when bad market data creeps in the stream.

EagleAi Market Data Risk module can process streaming data as an input. As it processes this data, it would self-learn about the characteristics of each symbol such as the typical price increase, the typical bid-ask spread at different times of the day, typical volume, typical values for the greeks if it were an option symbol etc. When EagleAi detects a huge deviation from the symbol’s patterns such as the one in this scenario, it can set a flag on the market data stream so that algos can choose to ignore this market data. EagleAi Market Data risk uses many different AI (Artificial Intelligence) techniques to detect these anomalies to compute a proprietary score named “E-score”. False positives are significantly reduced by flagging it only when EagleAi score is above a high threshold. EagleAi Market Data risk module can prevent trading decisions based on bad market data to prevent financial losses to the Client and maximize profits.

EagleAi – detects abnormal market data at lightning speed.
The EagleAi Market Data Risk module is an ensemble of Anomaly Detection techniques to detect abnormal market data across thousands of symbols.
Book a demo today @ https://eagleai.com/tradewatch-market-data-risk/

EagleAi is a collection of enterprise scale AI engines, which will watch your business’s back and prevent major losses Trading Risk, Fraud Risk, Compliance Risk, position exposures (TradeWatch Position Risk), and Market Data Risk.