To help institutions comply with guidelines surrounding operational risk, Shigatsu Baka Financial Technologies (SBFT) has developed a new technology system to help firms cope with "Fat Finger" trading errors, a common problem on trading desks.

To help institutions comply with guidelines surrounding operational risk, Shigatsu Baka Financial Technologies (SBFT) has developed a new technology system to help firms cope with "Fat Finger" trading errors, a common problem on trading desks.

Trade-entry errors can cost financial institutions many billions of dollars. Unlike other branches of risk management, which can be quantified to some degree (e.g. VaR for market risk, CVA for counterparty credit risk, etc.), it is very difficult to predict or quantify Fat Finger trading errors – and when they happen, they can be very costly indeed.

Examples of Fat Finger Errors include:

• October 2014, $600bn of mistaken orders
• A halt to Yen trading on CME (subs required)
• A 22222 error draining a pension fund
• A spike in the HSBC share price

SBFT's proprietary technology is driven by the Human Organisational Activities eXchange database. In a recent white-paper, SBFT demonstrated a strong relationship between the occurrence of Fat-Finger errors and the traders' social activities close to the time that the Fat Finger mistakes occurred. In particular, correlation was shown between the occurrence of Fat-Finger mistakes and the following types of events:

• Hangovers/tiredness from birthday parties
• Hangovers/tiredness from bachelor/bachelorette parties
• Hasty trade entries made before rushing-out-of-the-door to catch the flight for a vacation

The SBFT database collects the following reference data from traders:

• Date of birth
• Friends' dates of birth (with a weighting factor proportional to the closeness of the friendship)
• Dates of upcoming friends' weddings (weighted by a 3X multiplier if trader is the best-man / maid-of-honour)
• Dates of upcoming holidays and vacations
• Blood Alcohol Content (via a finger tip sensor in real-time)

Using a predictive algorithm acting on this reference data, the SBFT engine crunches the numbers and provides a quantification of the potential future Fat-Finger loss.  SBFT plan to market this data to exchanges and trading firms as a real-time guard against Fat Finger Errors, they will license the data in return for each licensee gathering and adding their own metrics to the data set. Traders will be matched against the database and issued with a FatFinger(tm) score which they must display on their traders badge at all times.

Their badge will be embedded with an RFID tag and wrist strap, integrated via Bluetooth 4 LE. The wrist strap will provide an electrical stimulation using the specific trigger criteria from the data gathered above. The stimulus will be non-fatal but strong enough to cause a jump and remind them to avoid these sorts of systemic errors.