Fraud is a word to smack scare into the hearts of any stockholder, who lean to take an organization’s economic figures at face value. But periodically they found themselves burned when unduly nasty or even forged accounting leads to tragedy. Enron is the typical case of an apparently rock-solid corporate behemoth that was, in reality, a dainty edifice of dodgy numbers and accounting subterfuge. More newly, one Canadian pharmacy group has lost about $80bn of its value over accounting interests. The company recently said that its internal accounting review had found nothing that would compel it to restate its earnings, helping its shares regain their footing, but many big-name investors are still nursing massive damage. Can mining of data about organizations help traders to investigate issues early?
Economist believed and has developed a model that scrutinize for potential issues. It mines the Securities and Exchange Commission’s database of companies condemned for accounting problems — examining how banks, trading firms and something that regulates is increasingly swinging to novel technological solutions to reveal market misdeed. Any bank’s significant analysts are ones looking to strap modern technology and data mining to exhibit potential problems. Regulators are also visualizing to capitalize on latest advances in computing and machine-learning algorithms to independently scan financial markets and company reports for signs of fraud or abuse. This is the future of fraud detection, as stated by a managing director at Control Risks’ compliance and forensic accounting division.
Big data and even more sophisticated algorithms will become more common among a variety of organizations in the finance industry. In other words, technological advances and progressively complex markets may make an investor’s job tough and more complicated, but may also offer some potentially powerful solutions to keeping financial markets clean.