HOUSINGNEWS REPORT
HOUSING PRECOGS: BIG DATA PREDICTIONS BEYOND HEURISTIC HUNCHES
BIG DATA DEFINITIONS: DISTINCTIONS WITH A DIFFERENCE
“Artificial intelligence, big data and machine learning are helping us reduce risk and fraud, upgrade service, improve underwriting and enhance marketing across the firm. And this is just the beginning.”
Artificial Intelligence : A term coined in 1955 by computer scientist John McCarthy, “artificial intelligence” can be described as the ability of computer systems to complete tasks historically done by people. Think of speech recognition as well as practical applications such as stock trading, the ability to read x-ray images, or fit eyeglasses. Data : An old business theorem says “that which can be measured can be managed.” The result of such efforts include measuring, counting, mapping, and numbering. Data may have value by itself (think of the score from one football game) but data becomes powerful when individual items can be processed together to reveal trends, directions, totals, averages, etc. Deep Learning : “Deep learning,” explains Mathworks, “is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands- free speakers.” Big Data : One football game by itself has meaning, it’s data. The games played over an entire season represent more data. The results from thousands of games played since 1960 are big data – but only after they have been arranged, examined, compared, poked, and messaged. Machine Learning : Machine learning can best be compared with the Borg, aliens from the Star Trek franchise who assimilate other cultures. As they explain, “we are the Borg. Your biological and technological distinctiveness will be added to our own.” In a similar sense, machine learning takes what it has learned today and adds that data to what it has accumulated before to create a new data set and what might be called bigger data.
JAMIE DIMON JP MORGAN CHASE
CHAIRMAN AND CEO IN HIS 2018 SHAREHOLDER LETTER
bombing done from planes in conflict is less costly? No, it is probably more costly, but 1,000 times more effective. I believe the same applies to targeted marketing, rather than spending a little on a lot of people, spend more on the right people. This concept applies to both direct marketing and digital marketing, even though it is somewhat counter intuitive for digital marketers who usually focus on reach rather than depth. As the saying goes ‘you can’t bet too much on the winning horse!’” Democratizing Disruption Data-based marketing is now like pollen, it’s everywhere and to make such systems work we have vast and growing armies of technologists. reduce risk and fraud, upgrade service, improve underwriting and enhance marketing across the firm,” JP Morgan Chase Chairman and CEO Jamie Dimon explained in his 2018 shareholder letter. “And this is just the beginning.” “Artificial intelligence, big data and machine learning are helping us
Predictive Modeling : The use of existing data to forecast future trends.
In banking, as one example, McKinsey & Company reports that “AI’s ability to detect anomalies among millions of transactions helps bank risk officers eliminate false positives that are a drain on productivity.”
JP Morgan has almost 50,000 technologists so where’s the opportunity for small firms?
The answer is that not only is our technological base growing it’s also being democratized. Fintech — financial technology — is everywhere
At first it might seem as though big data is reserved for big players.
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MAY 2018 | ATTOM DATA SOLUTIONS
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