The present global economic environment presents numerous difficulties to fintech institutions in optimising risk control. The decline in economic growth coupled with lower profit margins implies that risk control needs to be more efficient with simultaneous reduction in its costs.

To remain competitive and profitable in this era, fintech companies and other financial institutions need to rethink their risk control methodology. It is essential that they revaluate their organisational structure and the resources they allocate for controlling risk, which in turn plays a vital role in optimising the business outcomes.

Leveraging the latest digital technologies in areas such as cognitive analytics, Big Data analytics, Robotic Process Automation and advanced analytics, will have far reaching effects in the proactive identification of potential risks. Identification is always going to be the first step in managing risk.

It is also important to understand the difference between risk management and risk control.

Risk management – the definition

This is a broad concept, which deals with analysis, identification and treatment of risk. It is about making decisions and formulating strategies to tackle existing and future risks. Fintech companies can choose to accept, share, transfer or avoid individual risks completely.

Risk control – the definition

Risk control is an integral component of risk management, which is focused on the reduction or elimination of risks by implementing different procedures, policies and automatons. The operational processes designed to control risk can involve assessment and change in policies. Risk control limits or removes the probability of loss in assets and revenues. As a result, risk control becomes essential in achieving the business outcomes desired by fintech companies.

Avoidance is an effective method of risk control because it involves the complete elimination of individual risks by not involving assets in investments with potential downsides. The problem with avoidance is that some of the probable investments might turn out to be exceedingly profitable. As a result, fintech companies will miss the opportunity to increase their revenue.

Diversifying capital investments is a more balanced approach to risk control. Here, fintech companies can invest in a wide variety of asset sets. So, diversification reduces risks without compromising greater revenues and profits.

The interdependence of risk control and optimised business outcomes

All fintech institutions need to make their risk control activities holistic to attain the desired outcomes for their business. Vulnerability to risk and inefficient mechanisms to deal with potential threats can lead to organisational collapse. At the very least, small incidents can shake the confidence of investors as well as clients. Risk control methods of the highest quality will provide fintech companies with the stability and security needed to optimise business outcomes.

Problems in optimising risk control and business outcomes

Fintech companies need to tackle various types of risks to optimise their business outcomes. Avoiding fraud and minimising its possibility, assessing risks with credits and money laundering are among the biggest challenges that fintech companies face in risk control. Here is an assessment of the significant problems in risk control:

Controlling fraud

Fraudulent activities carried out by cybercriminals as well as customers have increased, and fintech companies need effective practices to deal with it. Identifying and mitigating fraud has become tougher and more important than ever before.

Credit Management

It is essential for fintech companies to improve their predictive capacity for the efficient management of credit given to customers and clients. Here, avoiding risk completely isn’t an option and reducing its possibility is the best approach.

Money laundering

All finTech companies need to identify transactions which are potentially indicative of attempts at money laundering. The less stringent regulations placed on fintech companies along with their less formal organisational structure have made them the ideal platform for launderers to accomplish their money laundering activities.

Market and commercial loans

Irrespective of their market sectors, fintech companies need top-notch simulations and predictions. Liquidity, exchange rates and interest rates form the basis of predicting the viability of all market and commercial loans.

Operational risks

For fintech companies, reducing operational risks requires great control of their interactions with all clients. The modus operandi of financial institutions also needs to be examined to eliminate operational risks.

Fintech companies need to place equal focus on each of these areas in order to optimise their business outcomes.

How Big Data can help in optimising risk control and business outcomes?

The risk control efforts of fintech companies can receive a tremendous boost by the effective use of Big Data analytics. With Big Data they will have access to all the information necessary to devise their risk control practices and modernise them to meet all challenges. It will also be essential to update these methods from time to time to ensure they remain relevant and effective.

Big Data analytics utilise external as well as internal data for identifying fraud and reducing the damage it does. As a result, risk control methods for fintech companies become better. Credit risk management is also a part of risk control which benefits with the use of Big Data analytics by enhanced predictive capacity for fintech companies.

When it comes to the prevention of money laundering activities, Big Data analytics can be extremely beneficial to fintech companies as well as traditional financial institutions. It helps to identify such incidents with the use of algorithms designed for spotting anomalies and deviations from a specific pattern. The fact that Big Data analytics provide real time reports of suspicious activities gives fintech companies the opportunity to prevent attempts at money laundering.

In the case of operational risk control, Big Data analytics will enhance the overall safety levels by giving fintech companies the desired knowledge and control when they interact with different clients. Big Data analytics creates a robust predictive model for risk control that contributes substantially to the optimisation of business outcomes. An integrated risk management system which places the right amount of emphasis on risk control will bring tremendous benefits to fintech companies in all market sectors. And, it can’t be achieved without Big Data analytics.

How Spyrosoft optimises business outcomes by implementing improved risk control methods?

The use of relevant Big Data analytics services combined with AI and ML technologies offered by Spyrosoft can ensure the effectiveness of your risk control practices. The quality of our services in these technologies focuses equally on the predictive capacity of internal as well as external datasets.

At Spyrosoft, we are constantly on the lookout for the latest sources of information and data aimed at bringing greater accuracy in predicting user behaviour. The real-time deployment of solutions after the identification of suspicious activities related to fraud and money laundering will give substantial efficiency to your risk control setup. High-quality and accurate predictive capacity is the hot topic as far as credit risk control is concerned and this is one of the areas where Spyrosoft dedicates significant effort.

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About the author

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Sophie Smith

Marketing Director at Spyrosoft Ltd.