7 Ways Fintechs Use Machine Learning to Outsmart the Competition
It has been said that to give a man a fish is to feed him for a day, whereas to teach a man to fish is to feed him for life. Forward-looking financial service companies are similarly finding that giving computers instructions is not nearly as fruitful as teaching them to write their own. From assessing credit risks to beefing-up the security of their own networks, fintech startups, in particular, are turning to machine learning finance-based solutions in order to work smarter rather than harder.
Big Banks Taking Notice
Considering that over 200 leading financial institutions will attend the upcoming October 2016 Machine Learning Fintech Conference, investment in this subset of artificial intelligence (AI) seems to be a wise move, indeed, for companies that don’t want to be left behind. With leading banks starting to invest in AI, and machine learning in particular, fintech companies will be significantly disadvantaged if they fail to do likewise. After all, one cannot hope to disrupt the competition unless one takes the technological high road.
Why large financial institutions are interested in this technology is the same reason they are interested in anything: machine learning, properly applied, can significantly improve the bottom line. In fact, there is a manifold advantage for companies that embrace machine learning technology, both in terms of replacing legacy systems and when developing enterprise or custom solutions. With the ability of machine-learning-based applications to catch costly errors, to improve efficiency, to augment decision-making processes, and to improve the customer experience, machine learning offers benefits on the front end, the back end, and everywhere in the middle. But it all comes at a cost, of course.
Whether a financial service provider purchases a solution among those increasingly-available on the market, or invests in custom development, the current cost of implementing any AI platform must be considered—not only in terms of software, but, in many case, hardware cost, also. However, as with any good investment, the payoff far outways the initial expense, as costs are reduced and profits are realized over time. For some applications such as fraud prevention, the payoff can be more immediate, as we shall see.
Not Just Another Buzzword
Unlike so many hyped technologies and overrated buzzwords, machine learning is not going away — probably ever. The ability of computer programs to learn on their own and improve over time is an inevitable step in the advancement of computer technology, and it can only become more prevalent. While AI and machine learning — its more advanced offspring — have been around for decades, recent breakthroughs have catapulted machine learning out of the R&D lab and into mainstream applications. Google, Facebook, and Amazon, for example, are already using machine learning to enhance the customer experience and to strengthen data security.
Although we will leave the mechanics of how machine learning works to the tech blogs, understanding the basic concept can help us see the potential of machine learning. In a proverbial nutshell, machine learning uses algorithms, or a set of rules rather than a set of specific programming instructions, in order to analyze information and make decisions. The information can be anything from customer behavior when searching for a loan online to complex data used to determine credit worthiness. Regardless, the power of machine learning lies in its ability to modify its own code and to make better, faster, and more accurate decisions as time goes on. Regular computer programs cannot do that.
Applications for Machine Learning in Finance
Let’s now look at how machine learning is impacting elements of the financial sector.
Financial service providers have no greater responsibility than protecting their clients against fraudulent activity. Financial fraud costs Americans, alone, $50 billion annually. Old ways of keeping clients’ accounts secure are no longer good enough. With every advancement in data security, criminals have stepped up to the challenge. To protect clients’ data against increasingly sophisticated threats, institutions and companies must stay one step ahead of hackers. Machine learning enables applications to thwart security breaches by out-thinking the criminals.
By comparing each transaction against account history, machine learning algorithms are able to assess the likelihood of transaction being fraudulent. Unusual activities, such as out-of-state purchases or large cash withdrawals, raise flags that can cause the system to introduce steps to delay the transaction until a human can make a decision. In many cases, depending on the nature of the attempted transaction, a purchase or withdrawal attempt may be automatically declined by the system.
Unlike a human agent, the algorithm is able to quickly weigh the transaction details against thousands of data points and make a determination whether or not the attempted activity is uncharacteristic of the account owner. And unlike non-AI software, machine learning programs learn from each action the account owner takes, and from each decision the software makes. Over time, the algorithms adjust themselves in response to changing habits on the part of the account owner.
In order for machine learning to be effective, it must be able to quickly access and digest large amounts of data. Realizing the value of machine learning, Amazon, Microsoft, IBM, and Google are each integrating machine learning capabilities into their cloud-based developer interfaces.
As criminals become more advanced in their strategies, only computer systems with access to big data and with the ability to think and learn will be able to stop them.
It should come as no surprise that machine learning technology can be a powerful ally in the quest for better risk management. While traditional software applications predict creditworthiness based on static information from loan applications and financial reports, machine learning technology can go further and also analyze the applicant’s financial status as it may be modified by current market trends and even relevant news items.
By applying predictive analysis to huge amounts of data in real time, machine learning technology can detect rogue investors working in unison across multiple accounts — something that would be nearly impossible for a human investment manager.
Efficiency is another benefit of machine learning. By assuming a substantial amount of the burden to monitor accounts, machine learning systems enable investment managers to focus on more productive tasks, such as servicing clients.
Computer aided trading services have been around for some time. They allow investors to have an order placed when a stock reaches a predetermined price, and to sell when that price drops below a certain limit. By automating functions, such platforms make trading easier for large and small investor, alike. While they can even make recommendations based on automated analysis of market trends, they have limitations.
In recent years, hedge funds have increasingly moved away from traditional predictive analysis methods and have adopted machine learning algorithms for predicting fund trends. Using machine learning, fund managers hope to identify market changes earlier than is possible with traditional investment models.
The potential of machine learning technology to disrupt the investment banking industry is being taken seriously by major institutions. JPMorgan, Bank of America, and Morgan Stanley are developing automated investment advisors, powered by machine learning technology. Wise fintech companies will likewise follow suit.
Poor customer service remains one of the chief complaints among consumers, regardless of the industry. Originally, the complaints centered on slow customer service, but with the universal utilization of automated phone support, customers are frustrated by not being able to speak to a human. For the innovative financial service company willing to invest in machine learning technology, this should not represent a problem so much as an opportunity.
The advantages of automated support systems include directing the customer to the correct department, giving them the option to resolve minor problems by using the automated interface, and keeping the customer from having to wait for someone to answer the phone — all without human interaction. The company benefits by not paying salaries to personnel who would handle these tasks, and the customer (supposedly) benefits by having their problem handled with the expeditiousness of modern computers. In theory, anyway.
In reality, many customers do not benefit from automated customer service when their problem is uncommon and not represented by a numeric option. While definitely an improvement, voice recognition often still fails to accommodate the unusual request, with “I’m sorry I’m not understanding you; please hold for assistance” being the stock response to the customer with a unique problem.
The solution, as provided by machine learning technology, is not to replace automated customer support systems, but to make them better. The tremendous power of machine learning technology to access data, recognize patterns, and interpret behavior means that the technology can be used to create automated customer support systems that mimic a human agent, with the ability to understand and respond to uncommon concerns. By making phone and online customer support portals more human-like, financial institutions can provide efficient support that reduces customer blowback.
Additionally, machine learning technology can help customers make better product selections by weighing previous account activities against current data provided by the client and from elsewhere. Product or service recommendations can be made directly to the client, or through a financial advisor. The result is a better-informed client whose time is not wasted by inappropriate offerings.
One of the most important paybacks for companies that invest in machine-learning customer service is the ability to better understand the needs of each individual customer, which is always a good thing.
No company, fintech or fish market, operates without sound management. And for management to be “sound,” it must function effectively and efficiently. Machine learning technology, perhaps surprisingly, can help executives and managers to perform their jobs with greater ease than ever before.
We are talking about digital assistants, and the competition to develop a winning platform is fierce. Google, Apple, Facebook, and Microsoft each have their own version of the virtual secretary. Google’s Allo, Apple’s popular Siri, Facebook’s M, and Microsoft’s Cortana currently represent the state-of-the-art in digital helpers. Each targets a certain market, and each has its own advantages and limitations. For the innovative fintech startup intent on making its own toys, it is a good time to consider the competition and then take away what will work best in the business office of a financial institution.
Machine learning technology includes several functionalities that can be useful for developing a custom digital assistant. Namely:
- Speech recognition
- Access to big data
- Powerful data analytics capabilities
- The ability to integrate with social media, email, and third-party applications and platforms
- Pattern and behavior recognition
Although this list is far from inclusive, it is easy to see how machine learning technology can replace certain executive duties, while augmenting others.
Whether a financial service company chooses to invest in the development of a virtual assistant platform for its own operation, or for the purpose of offering the platform as part of a service package for their clients, the return on the investment is likely to be substantial.
Having read of the many ways in which machine learning can keep accounts secure, improve risk management, and offer investment strategies, you might not expect the technology to also be a good marketing tool. On the contrary. The ability to make predictions based on past behaviors is fundamental to any successful marketing effort. By analyzing web activity, mobile app usage, response to previous ad campaigns, machine learning software can predict the effectiveness of a marketing strategy for a given customer.
With the online marketing power of Google, now augmented by machine learning, it is possible for developers working in the financial sector to create smart tools that make the job of marketing executives easier than ever.
The potential of machine learning to supercharge the marketing industry has lead to a spurt in machine-learning-based advertising startups in the last year. So many, in fact, that 2015 saw no less than ten acquisitions in this space, including the Twitter acquisition of TellApart for more than $530M. Clearly, Big Money has its eye on machine learning as the next big thing in marketing.
Among the top considerations for any network administrator or data security professional is how to recognize suspicious patterns occurring across their networks. The challenge to identify such patterns lends itself perfectly to the capabilities of machine learning. The power of intelligent pattern analysis, combined with big data capabilities, certainly gives machine learning technology an edge over traditional, non-AI tools. One might go so far as to declare machine learning as the last hope of securing critical networks against professional and state-sponsored cyber attacks.
Despite costly and increasingly complex IT security platforms, even big name organizations seem defenseless against modern cyber attacks. So far, hackers have breached the network security of such mammoths as the U.S. Department of Justice, the U.S. Democratic National Committee (DNC), the Internal Revenue Service, Snapchat, LinkedIn, and Oracle. Clearly, organizations need to rethink their approach to cyber security. Existing systems do not necessarily need to be abandoned, but the additional layer of wizardry which machine learning security can provide is the next step in the evolution of network security.
Adding credence to the notion that machine learning is a viable network security tool, Microsoft is investing heavily in its own “deterministic” machine learning/big data platform. Using statistical analysis of baseline metrics, along with historical data from “bad” behaviors, computer scientists are constructing models that can identify “anomalous” or abnormal behaviors.
While not every organization replicate the resources of IBM, the lessons learned from the tech giant will help fuel independant development in the fintech/financial IT security space.
How Ignite Can Help
To foresee all the ways in which machine learning will impact the financial sector would require the highly sought after — and yet unperfected — technology, the crystal ball. For lack of it, we can only envision how the technology can be used to solve problems now and in the near future. The seven applications we have discussed surely represent a technology in its infancy. No doubt, innovative fintech startups are already adding to the list.
As a financial service provider, finding fresh, innovative ways of serving your clients and customers is key to your longevity, and knowing how to access and use big data is only the start. Today’s landscape demands that you do more. You must bridge new technology with systems that are familiar to your users, while improving the level of service minute by minute. That’s where Ignite Outsourcing comes in.
With six R&D teams throughout Europe, Ignite Outsourcing has the resources and experience to develop the solutions your business needs to succeed in the financial services sector. Whether you need help implementing a third-party machine learning solution, or if you require a custom solution, we can provide you with a no-compromise solution at a lower cost than you might expect.
Why not contact us today for a free consultation?