Machine learning (ML) is one such technology that has tremendously benefited financial services and banking industry. Bad decision making costs organization in terms of time, profitability and reputation. But the real cost of bad decisions is neither losing revenue nor reputation; it is an opportunity to delight customers. The chances are that organizations are more likely to make more bad decisions than good ones, thanks to the onslaught of data.
The best way to reduce the cost of bad decision making is to find and fix it, before decision-making team acts on it, machine learning as a subset of data science exactly does that by helping financial services draw insights and make accurate predictions. Thanks to ML, organizations are now able to make better business decisions and more intelligent courses of action with minimal human intervention
Some of the reasons why financial services should be considering machine learning:
- Machine learning presents a valuable opportunity for organizations to minimize operational costs and everything that matters, thanks to process automation.
- Today, increasing revenues is understandably a top concern, thanks to machine learning, more organizations are discovering how it is enabling them to tackle the challenge of data-driven disruption including better productivity and enhanced user experiences.
- Managing multiple compliances and stringent laws surrounding regulatory can be complex and difficult. Many companies face these challenges because they may have deployed their legacy compliance programs. ML covers all of these areas in depth with a systematic approach to meeting the needs of Compliance – often for the price of a single full-time resource.
Machine learning is good but there’s a catch
The main challenge of moving to ML is how to meet challenges like inaccessible data and sensitive data security, infrastructure requirements for testing and experimentation and inflexible business models to name a few.
Prime makes it easier to successfully leverage the machine learning—your way, at your pace.
Data empowers organizations to disrupt competitors and find new revenue streams. ML offers the scale and agility needed to work with today’s data volume at today’s business velocity
How machine learning helps finance services
In many organizations, process automation is still an intensive manual task, the teams involved in the task get their things done using their current tools, which are often reactive and standalone. This challenge only increases with customers demanding a digital experience that’s customized, offers ease of use and consistent across all channels.
To do this, they should leverage technology that will allow them to replace manual work, automate routine tasks that way finance companies can deliver seamless support across digital channels such as online self-service, social, messaging and chat. Process automation is among the widely common applications of machine learning in finance.
Every financial service organization’s biggest challenge
What’s the finance industry’s’ biggest challenge? For most organizations, it is replacing manual operations, automate tasks that are repetitive and find ways to maximize productivity. Machine learning enables enterprises to unleash the full power of their data to optimize costs, improve customer experience and scale-up services. With chatbots, call-center automation, documentation automation you’re able to meet business objectives – whether you’ve cartload of legal documents or manual review of agreements.
Despite a strong focus on ML, many organizations do not believe they are making most out of this technology for the following reasons:
- Maintaining their legacy systems could be causing them to fail.
- The lack of DS/ML engineers is another worrying concern.
One of the reasons why your legacy systems might need a facelift includes:
Maintaining legacy system, far from easy:
To keep a technology like machine learning running necessitates the modernization of legacy processes. Because if you are depending on systems that can be only be supported by resources that are fast becoming obsolete, then the chances are that nothing can fix them. Fortunately, this is easy to achieve with the right set of automation platform tools and strategies. Prime provides a range of flexible, incremental legacy modernization services and replacement strategies that balances cost, risk and time in a way that meets each customer’s unique requirements.