Artificial intelligence (AI) is the key for financial service companies and banks to stay ahead of the ever-shifting digital landscape, especially given competition from Google , Apple , Facebook , Amazon  and others moving strategically into fintech. AI startups are building data products that not only automate the ingestion of vast amounts of data, but also provide predictive and actionable insights into how people spend and save across digital channels. Financial companies are now the biggest acquirers of such data products, as they can leverage the massive data sets they sit upon to achieve higher profitability and productivity, and operational excellence. Here are the five ways financial service companies are embracing AI today to go even deeper inside your wallet.
Your Bank Knows More About You Than Facebook 
Banks and financial service companies today live or die by their ability to differentiate their offering and meet the unique needs of their customers in real-time. Retention is key, and artificial intelligence is already disrupting what it means for financial service companies to “know the customer.” Google, Facebook, Twitter, and other walled gardens already deeply understand this, which is why they are so keen to collect massive amounts of data on their users, even if they don’t have fintech infrastructure yet.
So how does your bank know more about you than Facebook? Using AI platforms , they can bridge customer data across multiple accounts – including bank, credit, loans, social media profiles, and more – and give them a 360-degree view of the customer. Once they have this, predictive applications suggest in real-time the “next best” offer to keep the person happy based on their spending, risk tolerance, investment history, and debt. For example, based on one transaction – a mortgage – financial companies use AI to recommend a checking account to pay for the mortgage, credit cards to buy furniture, home insurance, or even mutual funds that are focused on real estate. Financial services companies can now also predict customer satisfaction and dissatisfaction, allowing them to intercept consumer churn before it happens by offering exclusive deals or promotions before the person gets angry.
Credit “Risk” Is Becoming Competitive Opportunity 
A limited amount of data is used for credit risk scoring today, and it’s heavily weighted toward existing credit history, length of credit usage, and payment history. Naturally, this results in many qualified customers – or anyone trying to access credit for the first time – being rejected for loans, credit cards and more. Credit card companies, including Amazon , are realizing there is a big revenue opportunity that is missed by the current credit assessment system. With AI, employment history data, social media data, shopping and purchasing patterns, and are used to build a 360-degree view of the credit “opportunity” as opposed to pure risk. Even better, AI data products can provide real-time updates of credit scores based on recent employment status changes or transactions, so that your credit score is not a fixed number but something that evolves. With this capability, banks and financial services companies are finding overlooked or untapped credit opportunities that even the most sophisticated tech company is missing.
Predict the Next DDOS Attack 
The distributed denial-of-service (DDOS) attack against  Dyn in October brought to the public forefront the scale and severity of cyber attacks. In the financial realm, security breaches and cyber attacks are not only costly, but also have a damaging impact on brand trust and customer loyalty. Experts and analysts agree that such DDOS attacks will become more prevalent in the future, in part because current cybersecurity practices are built upon rules-based systems and require a lot of human intervention. Many of the current cybersecurity solutions in market are focused on detection, as opposed to prevention. They can tell you an attack is happening, but not how to predict one or what to do once it’s discovered.
Leveraging AI platforms, banks, credit card companies, and financial service providers are beginning to predict and prevent such cyber attacks with far greater precision than what’s in use today. Using traffic pattern analysis and traffic pattern prediction, AI data products inspect financial-based traffic in real-time and identify threats based on previous sessions. Effectively, this means that a financial company can shut down harmful connections before they compromise the entire website or server. Importantly, as more data is ingested, the AI data product evolves and gets smarter as the hacker changes its methodology. This takes the notion of prevention to a whole new level, as it anticipates the bad actors’ next move.
Putting an End to Money Laundering 
The estimated amount of money laundered globally  in one year is 2 to 5 percent of global GDP, or upwards of $2 trillion in USD. Efforts to combat money laundering are never-ending, as criminals find new ways to stay ahead of law enforcement and technology. Customer activity monitoring is currently done through rules-based filtering, in which rigid and inflexible rules are used to determine if something is suspicious. This system not only creates major loopholes and many false positives, but also wastes investigators’ time and increases operational costs. AI platforms can now find patterns that regular thresholds do not detect, and continuously learn and adapt with new data. Because false positives are reduced, investigators then focus on true anti-money laundering activities to create a more efficient, accurate solution, and at the same time reduce operational costs. Suspicious activity reports are finally living up to their name of truly documenting suspicious behavior as opposed to random red flags in a rules-based system.
Biometrics-Based Fraud Detection 
Fraudulent credit card activity is one area where artificial intelligence has made great progress in detection and prevention. But there are other interesting applications that are strengthening financial services companies’ overall value proposition. Account origination fraud – where fraudsters open fake accounts using stolen or made-up information – more than doubled in 2015. That’s because there’s no way to prove with absolute certainty that the person on the mobile device is who they say they are. AI technologies are being developed to compare a variety of biometric indicators – such as facial features, iris, fingerprints, and voice – in order to allow banks and financial service companies to confirm the user’s identity in far more secure ways than just a pin number or password. Mastercard, for example, unveiled facial recognition “security checks”  for purchases made on mobile phones. Given its potential to protect user’s identities from being stolen or abused, biometrics in the context of banking and financial services may face fewer regulatory hurdles than practices undertaken by Facebook and Google, both of whom have faced class action lawsuits . This is allowing financial services to move much faster in the field of biometrics.
Beyond the Wallet 
The tech giants are in an arms race to acquire as many AI and machine learning startups as possible. But the one thing they don’t have yet, and financial services companies do, are massive amounts of financial data. Up until now, financial services companies required a tremendous amount of experience and human judgment in order to analyze this financial data and provide cost-effective, competitive products and services. However, by adopting “out-of-the-box” AI data products that can ingest huge amounts of data, banks and financial services companies are making valuable predictions and insights in real-time that drive revenue and reduce inefficiencies. The five applications above are not simply isolated use cases, but bellwethers of how intimately AI will be directly tied to enterprise-level financial strategy.

Source: paymentsjournal.com