The future of financial technology – part 3: The strategic future of data

 In Fintech

financial technology Submitted by Paul Sullivan via ModedaWeb

In the first article in this series, we looked at how financial technology is already changing the financial services industry before moving on to look at how automation will be essential to managing data analysis and operational efficiency.

In this third instalment, we will cover the shift from data as a basic resource to a strategic benefit.

Smarter, faster machines

Last week we touched on the need for autonomous software capable of making and executing decisions based on the analysis. Initial efforts will involve hard-coded events and thresholds that trigger automated action, but as data volumes continue to grow, and market conditions change, systems will need to be capable of maintaining themselves. Naturally artificial intelligence systems will develop rapidly to improve further autonomous rule creation and decision making.

Artificial intelligence and machine learning is extremely processor intensive, and the financial technology sector will need to develop new solutions that can cope. The Cloud is one approach to on-demand processing power, but each platform will need to carefully consider the strict compliancy issues associated with the financial sector before adoption. It may be that on-site equivalents will need to be developed and deployed in the longer term, often with custom configurations to provide the required power and efficiency.

Ushering in the cashless world

Contactless payment cards and digital wallets create a paradox; payment card systems and providers become more important, whilst simultaneously struggling to maintain relevance in a more crowded marketplace. Financial providers will need to use every advantage available to ensure that they become the default card of choice for their customers.

Fortunately the switch to virtual payments provides many more opportunities for data collection and mining, feeding back to the need for streamlined infrastructure. This data will also help to better tailor services to customers’ needs.

Aside from the currency side of cashless transactions, there will need to be a refinement of the associated meta information. Billing needs to be integrated into the checkout and transaction process, executed in real time to keep pace with customers’ expectations of instant completion. At this point existing data needs to be applied to the challenges of streamlining customer facing operations.

Tapping into alternative lending schemes

At the same time as alternative payment methods are breaking into the mainstream, so too are alternative lending schemes. P2P lending, like ratesetter.com and Zopa allow individuals to lend and borrow small sums at reasonable rates and bypass traditional banks. Alongside the platforms required to facilitate transactions, the data gathered needs to be put to good use.

P2P transactions provide additional insight into economic conditions, providing context that can be applied in other operations. Again, Big Data analysis will be an essential part of identifying valuable, actionable intelligence, and autonomic systems will help to capitalise on them.

The modern enterprise

The success of every financial business is governed by the data it has available. The quality, accuracy and freshness of data is inextricably linked to its value, but applied correctly and it will not only inform business strategy, but also drive it forwards.

With infrastructure, autonomy and artificial intelligence in place, finance organisations now have the technical basis they need to further increase efficiency and profitability. They are also well placed to move onto the next step, reduced inter mediation.

Disclosure:

Paul is DOM at Modedaweb a finance andfintechspecialist inbound marketing agency and a keynote speaker on marketing automation.

 

 

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Sergey Sanko
Sergey had started an IncomeClub after years of being an investment advisor for high affluent investors and managing fixed income securities. He is the lead investment advisor representative and holds a Series 65 license. Sergey earned his Executive MBA degree from Antwerp Management School.
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