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How data and artificial intelligence are being used in the finance industry

Big data and how we read it

The term “big data” is currently being used to describe how the use of predictive and user behaviour analytics, along with other advanced methods, extract value from a seemingly endless supply of information made available by the growth in technology and the availability of that information. The software and the minds behind creating these applications are the key players to bring about major changes in some of our biggest industries.

In the finance industry it has been the growth of fintech startups such as Cambr’s new Digital Banking Platform that has raised a disruption in traditional methods, moving it into newer and more efficient areas and methods of performance.

To look at how that transfers into our daily lives the huge amount of data available about each and every one of us is now being accessed by artificial intelligence software programs to explore everything we do; from our social media activity, what we buy online and where from, our search history, our messaging conversations and more, and from all that information the results are giving banks a deeper learning into what and how we spend; this gives them a far greater opportunity to sell us exactly what we need, whether we know what that is or not, and these selling opportunities are now also becoming automated, just as the gathering of the information was in the first place.

The use of artificial intelligence is already widely used in the exchange market. AI is learning and predicting patterns that make investing a lower risk with higher profit outcomes, and just like this a similar function in the banking industry has arisen. It has become not just apparent but necessary for traditional banks to maintain awareness of the sector and of the more efficient and affordable alternatives fintech businesses are now offering.

Bloomberg announced that trading from the results of big data analysis has grown over the past ten years and is now responsible for 40% of trading in Europe and 55% in the US. The companies using these new algorithms to search out profits are the ones who are leading the way and dominating the market.

So how are the banks going use this information in order to be more efficient and more competitive?

1. Machine learning and automation

Using artificial intelligence to understand and predict changes in data analysis that can update and improve its own algorithms is becoming a forward movement in developing the technology. These automated decisions are improved with the addition of more and more data. Automation in other areas of banking and finance is also improving business efficiency; underwriting, risk model development and reconciliation are also showing an increase in benefits.

2. Engage with public cloud based solutions

Keeping all the information so far in private cloud solutions has been a safe way for banking to keep information secure. Now that technology allows more secure ways of storing our information hybrid cloud use between private and public options will give access to required computations and also for the advance in new application development. The financial sector is going to need to utilise these methods of managing their big data to crack issues in updating, synchronising and governing data assets.

3. Further engagement with blockchain technology

Blockchain is probably the main component in the future of banking and financial services right now. The leading question is how it’s going to be utilised to match the fintech operations already employing its benefits. Will private or public blockchain use draw new issues in the legalities of using such a system? How will it compare to current and traditional transaction activities? And what are the ways forward of using blockchain information to tie in with big data and use the information to the bank’s advantage? This is going to be a big conversation topic in banking in 2018.

4. Detecting and eliminating fraud

Using AI to recognise patterns in detecting fraud cases and money laundering will reduce the need for manual monitoring. This should also help cut down the time spent keeping the regulatory agencies happy with a large percentage of the rules being written directly into the software to eliminate it at the source.

Data analytics and AI are going to be very important in detecting criminal activity, fraud, money laundering and more, so the banks are going to have to address using risk data aggregation and model risk as a primary focus.

5. Data governance

Making sure all the data involved is of high quality and of real use to the banks will be a substantial part of their continuing awareness into converting their findings towards the most appropriate responses and ROI. The solution here will be to manage critical aspects of the information to make positive results a more likely outcome by using cleaner data. This in turn will dictate which products and services are the most likely saleable products for their users.

6. The data team

Data scientists and engineers are going to be highly important members of the prediction process. The improvement in data management and analysis is going to have to be skilfully organised to utilise how the AI reads, analyses and uses the information it receives. The people who organise the computer systems and their software are going to become increasingly important as this part of the industry grows and will be a much more important member of the finance team than currently considered.

7. Integrating historical and current financial data

Banks and financial traders have always stored their own historical data of financial transactions but increasingly now they are using it alongside their real-time data to analyse not just patterns in spending by the business or individual but also any effects of additional influences at the time depicting additional customer behaviours and trading patterns.

8. Utilising the Internet of things

Initially not a highly considered opportunity for banking, the growth of data provided by these items over the more obvious mobile and online pools is becoming more relevant with patterns of spending and payment activities being shown through this further field of information. This streaming data can improve the efficiencies of financial operators, lowering costs and usage.

So what’s next?

We’re going to see more involvement in data analysis in banking, there are going to be big changes in movement using artificial intelligence systems to increase opportunities of higher ROI, and there is definitely going to be a rise in developers looking for all the ways to get ahead in all the financial sectors.

Big data management is fairly new in this industry but banks, insurance companies, investment groups and more are going to be some of the most prominent users of big data the world over, and finding better and more advantageous ways to utilise this increased and insightful information is the way they’re going to succeed.

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