Business

Cognitive-Computing

5 Ways Cognitive Computing is Disrupting Finance

The Rapidly Evolving World of FinTech

The financial world is undergoing relentless disruption. The vast majority of this change has been positive, helping to transform banking, investment, and wealth management from untrustworthy networks into transparent, customer friendly services. This has been largely facilitated by the application of innovative technology – not least of all cognitive computing. Cognitive computing refers to the simulation of human thought processes via machine learning techniques like pattern recognition and natural language processing. Collecting, analysing and using data is especially important for financial organisations, making cognitive computing incredibly applicable within the industry. According to IBM, 88 per cent of bank executives intend to invest in cognitive capabilities. But how is it disrupting finance?

1. Organisation
The more information a bank has about customers’ everyday lives, the more it can offer fine tuned services. Through cognitive computing, financial businesses have been able to collect personalised information about individual clients and use this to prompt and preempt spending. This has enabled the creation of nifty apps that sync users’ calendars with payments – for instance, reminding you to pick up a birthday card for your Aunt. Personal financial management demonstrates how intelligent automation can improve the relationship between customers and the financial services that they use. As well as prompting payments, apps can also caution against them, making sure that customers don’t exceed their budgets.

2. Advice
The application of cognitive computing within finance has enabled the development of clever conversational interfaces with the ability to sift through data and provide relevant answers to customer queries. An example of this would be a chatbot. Chatbots are AI powered assistants designed to streamline customer services and consequently improve CRM. Robo advisors offer similar advice but aren’t equipped with AI. They do, however, use algorithms to search through customer data and come up with pertinent suggestions. Despite uncertainty over the quality of conversational interfaces, more and more financial businesses are investing in these automated systems to ease the burden on customer service teams.

3. Cybersecurity
Cognitive technology mimics the way that humans perceive data, but obviously on a far greater scale. Pattern recognition allows it to sift through masses of data and detect anomalies, which is useful when identifying potentially fraudulent or fake transactions. By revealing hidden content or malicious software, cognitive computing is therefore key to security. Since high profile digital heists like the attack on the Bangladesh Bank or the Tesco Bank hack, ensuring the protection of financial data is vital.

4. Transparency
Financial companies are expected to adhere to complex regulations, ensuring that requirements are met. Without a concise knowledge of data policy and the strategy already used within the business, organisations can find it challenging to comply. Cognitive computing has made this a far easier task by tracking real time changes in complicated data laws and applying them to business infrastructures. By digesting policy documents at unprecedented speeds, companies can keep up with rapidly altered rules. This is clearly important for the business itself in terms of avoiding hefty fines, but also for the industry as a whole by encouraging good practise and compliance.

5. Trade
One of the key tasks for new FinTech companies and legacy players is to restore lost trust in trade. Algorithms have long been used to solve complex problems within trade, but add cognitive computing to the mix and these algorithms become fluid and responsive. Machine learning techniques have enabled real time, constant improvements in trading systems, allowing customers to be served faster. Deeper market insights have encouraged quicker decision making, consequently streamlining processes. Not only this, but artificially intelligent algorithms can also flag and investigate anomalies in data, which puts both clients and suppliers at ease.

Cognitive computing has proved beneficial for customers and companies alike. Algorithm enabled apps, digital advisors and improved cybersecurity have directly impacted how everyday consumers manage their money, as well as giving financial businesses the means to collate and use huge datasets. Cognitive computing doesn’t come without challenges, though. The use of robo advisors, chatbots and apps may be more efficient than chatting to (and paying for) human advisors, but a lack of face to face customer services could be damaging. The task now is for financial organisations to find a balance between automated technology and traditional infrastructures.